Vision–Language Models (VLMs) have shown strong performance on chart understanding, yet remain brittle to misleading visual encodings—cases where the underlying data and question stay the same, but design choices (e.g., inverted or truncated axes) induce a misleading visual impression. Prior work shows that supervising VLMs with human gaze improves attention alignment and accuracy, but it is unknown whether gaze also improves robustness to deceptive chart designs. We compare gaze-supervised and non-gaze-supervised VLMs under a set of controlled chart transformations that preserve question semantics while altering visual encodings, including axis inversions, truncations, and chart-type mismatches. Across manipulation categories, gaze-supervised models exhibit smaller performance fluctuations and fewer statistically significant prediction shifts than their non-gaze counterparts, particularly under transformations that alter temporal or axis encodings. These results suggest that learning where humans look can strengthen attention alignment and improve robustness to deceptive visualizations, underscoring the value of human-centric supervision.

In this paper, we introduce and apply Operations Research Question Answering (ORQA), a new benchmark, to assess the generalization capabilities of Large Language Models (LLMs) in the specialized technical domain of Operations Research (OR). This benchmark is designed to evaluate whether LLMs can emulate the knowledge and reasoning skills of OR experts when given diverse and complex optimization problems. The dataset, crafted by OR experts, presents real-world optimization problems that require multistep reasoning to build their mathematical models. Our evaluations of various open-source LLMs, such as LLaMA 3.1, DeepSeek, and Mixtral reveal their modest performance, indicating a gap in their aptitude to generalize to specialized technical domains. This work contributes to the ongoing discourse on LLMs’ generalization capabilities, providing insights for future research in this area. The dataset and evaluation code are publicly available.

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that leads to dementia, and early intervention can greatly benefit from analyzing linguistic abnormalities.In this work, we explore the potential of Large Language Models (LLMs) as health assistants for AD diagnosis from patient-generated text using in-context learning (ICL), where tasks are defined through a few input-output examples.Empirical results reveal that conventional ICL methods, such as similarity-based selection, perform poorly for AD diagnosis, likely due to the inherent complexity of this task.To address this, we introduce Delta-KNN, a novel demonstration selection strategy that enhances ICL performance.Our method leverages a delta score to assess the relative gains of each training example, coupled with a KNN-based retriever that dynamically selects optimal "representatives" for a given input.Experiments on two AD detection datasets across three opensource LLMs demonstrate that Delta-KNN consistently outperforms existing ICL baselines.Notably, when using the Llama-3.1 model, our approach achieves new state-of-the-art results, surpassing even supervised classifiers. 1

While in-context Learning (ICL) has proven to be an effective technique to improve the performance of Large Language Models (LLMs) in a variety of complex tasks, notably in translating natural language questions into Structured Query Language (NL2SQL), the question of how to select the most beneficial demonstration examples remains an open research problem. While prior works often adapted off-the-shelf encoders to retrieve examples dynamically, an inherent discrepancy exists in the representational capacities between the external retrievers and the LLMs. Further, optimizing the selection of examples is a non-trivial task, since there are no straightforward methods to assess the relative benefits of examples without performing pairwise inference. To address these shortcomings, we propose Detriever, a novel demonstration retrieval framework that learns a weighted combination of LLM hidden states, where rich semantic information is encoded. To train the model, we propose a proxy score that estimates the relative benefits of examples based on the similarities between output queries. Experiments on two popular NL2SQL benchmarks demonstrate that our method significantly outperforms the state-of-the-art baselines for the NL2SQL tasks.

We introduce CEMTM, a context-enhanced multimodal topic model designed to infer coherent and interpretable topic structures from both short and long documents containing text and images.CEMTM builds on fine-tuned large vision language models (LVLMs) to obtain contextualized embeddings, and employs a distributional attention mechanism to weight token-level contributions to topic inference.A reconstruction objective aligns topic-based representations with the document embedding, encouraging semantic consistency across modalities.Unlike existing approaches, CEMTM can process multiple images per document without repeated encoding and maintains interpretability through explicit word-topic and documenttopic distributions.Extensive experiments on six multimodal benchmarks show that CEMTM consistently outperforms unimodal and multimodal baselines, achieving a remarkable average LLM score of 2.61 (1-3 scale).Further analysis shows its effectiveness in downstream few-shot retrieval and its ability to capture visually grounded semantics in complex domains such as scientific articles 1 .

Ali Salamatian, Amirhossein Abaskohi, Wan-Cyuan Fan, Mir Rayat Imtiaz Hossain, Leonid Sigal, Giuseppe Carenini. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Intent, typically clearly formulated and planned, functions as a cognitive framework for communication and problem-solving. This paper introduces the concept of Speaking with Intent (SWI) in large language models (LLMs), where the explicitly generated intent encapsulates the model’s underlying intention and provides high-level planning to guide subsequent analysis and action. By emulating deliberate and purposeful thoughts in the human mind, SWI is hypothesized to enhance the reasoning capabilities and generation quality of LLMs. Extensive experiments on text summarization, multi-task question answering, and mathematical reasoning benchmarks consistently demonstrate the effectiveness and generalizability of Speaking with Intent over direct generation without explicit intent. Further analysis corroborates the generalizability of SWI under different experimental settings. Moreover, human evaluations verify the coherence, effectiveness, and interpretability of the intent produced by SWI. The promising results in enhancing LLMs with explicit intents pave a new avenue for boosting LLMs’ generation and reasoning abilities with cognitive notions.

We have developed a novel and comprehensive visualization design for group decision making that can support decision makers in modeling and comparing stakeholder preferences. We have implemented a prototype based on this design, and we have obtained preliminary evidence of its utility by means of a user study, wherein groups of individuals participated in a decision scenario that is held as a standard in group dynamics literature

Neural topic models can successfully find coherent and diverse topics in textual data. However, they are limited in dealing with multimodal datasets (e.g., images and text). This paper presents the first systematic and comprehensive evaluation of multimodal topic modeling of documents containing both text and images. In the process, we propose two novel topic modeling solutions and two novel evaluation metrics. Overall, our evaluation on an unprecedented rich and diverse collection of datasets indicates that both of our models generate coherent and diverse topics. Nevertheless, the extent to which one method outperforms the other depends on the metrics and dataset combinations, which suggests further exploration of hybrid solutions in the future. Notably, our succinct human evaluation aligns with the outcomes determined by our proposed metrics. This alignment not only reinforces the credibility of our metrics but also highlights the potential for their application in guiding future multimodal topic modeling endeavors.

Despite the challenges posed by data sparsity in discourse parsing for dialogues, unsupervised methods have been underexplored.Leveraging recent advances in Large Language Models (LLMs), in this paper we investigate an unsupervised coherence-based method to build discourse structures for multi-party dialogues using open-source LLMs fine-tuned on conversational data.Specifically, we propose two algorithms that extract dialogue structures by identifying their most coherent sub-dialogues: DS-DP employs a dynamic programming strategy, while DS-FLOW applies a greedy approach.Evaluation on the STAC corpus demonstrates a micro-F 1 score of 58.1%, surpassing prior unsupervised methods.Furthermore, on a cleaned subset of the Molweni corpus, the proposed method achieves a micro-F 1 score of 74.7%, highlighting its effectiveness across different corpora.

Discourse analysis studies the sentence organization within a document, aiming to reveal its underlying structural information.Existing works on dialogue discourse parsing mostly use encoder-only models and sophisticated decoding strategies to extract structures.Despite recent advances in Large Language Models (LLMs), applying directly these models on discourse parsing is challenging.To fully leverage the rich semantic and discourse knowledge in LLMs, we propose to transform discourse parsing into a generation task using a text-to-text paradigm.Our approach is intuitive and requires no modification of the LLM architecture.Experimental results on STAC and Molweni datasets show that a sequence-tosequence model such as T0 can perform reasonably well.Notably, our improved transitionbased sequence-to-sequence system achieves new state-of-the-art performance on Molweni.Furthermore, our systems can generate richer discourse structures such as graphs, whereas previous methods are mostly limited to trees. 1

There has been increasing interest in non-invasive predictors of Alzheimer's disease (AD) as an initial screen for this condition. Previously, successful attempts leveraged eye-tracking and language data generated during picture narration and reading tasks. These results were obtained with high-end, expensive eye-trackers. Instead, we explore classification using eye-tracking data collected with a webcam, where our classifiers are built using a deep-learning approach. Our results show that the webcam gaze classifier is not as good as the classifier based on high-end eye-tracking data. However, the webcam-based classifier still beats the majority-class baseline classifier in terms of AU-ROC, indicating that predictive signals can be extracted from webcam gaze tracking. Hence, although our results indicate that there is still a long way to go before webcam gaze tracking can reach practical relevance, they still provide an encouraging proof of concept that this technology should be further explored as an affordable alternative to high-end eye-trackers for the detection of AD.

We introduce PRIMERA, a pre-trained model for multi-document representation with a focus on summarization that reduces the need for dataset-specific architectures and large amounts of fine-tuning labeled data.PRIMERA uses our newly proposed pre-training objective designed to teach the model to connect and aggregate information across documents.It also uses efficient encoder-decoder transformers to simplify the processing of concatenated input documents.With extensive experiments on 6 multi-document summarization datasets from 3 different domains on zero-shot, few-shot and full-supervised settings, PRIMERA outperforms current state-of-the-art dataset-specific and pre-trained models on most of these settings with large margins.1

The multi-head self-attention mechanism of the transformer model has been thoroughly investigated recently.In one vein of study, researchers are interested in understanding why and how transformers work.In another vein, researchers propose new attention augmentation methods to make transformers more accurate, efficient and interpretable.In this paper, we combine these two lines of research in a human-in-the-loop pipeline to first discover important task-specific attention patterns.Then those patterns are injected, not only to smaller models, but also to the original model.The benefits of our pipeline and discovered patterns are demonstrated in two case studies with extractive summarization and topic segmentation.After discovering interpretable patterns in BERT-based models fine-tuned for the two downstream tasks, experiments indicate that when we inject the patterns into attention heads, the models show considerable improvements in accuracy and efficiency.

With a growing number of BERTology works analyzing different components of pre-trained language models, we extend this line of research through an in-depth analysis of discourse information in pre-trained and finetuned language models.We move beyond prior work along three dimensions: First, we describe a novel approach to infer discourse structures from arbitrarily long documents.Second, we propose a new type of analysis to explore where and how accurately intrinsic discourse is captured in the BERT and BART models.Finally, we assess how similar the generated structures are to a variety of baselines as well as their distributions within and between models.

Group decision making occurs when individuals collectively choose from a set of alternatives based on individual preferences. In these ubiquitous situations, it can be helpful for decision makers to visually model and compare stakeholder preferences in order to better understand others' points of view and reach consensus. Although a number of collaboration support tools allow preference inspection in some form, they are rarely based on a comprehensive understanding of the needs of group decision makers. The goal of our work is to study these demands, develop abstractions to model them, and create a framework to inform the design and assessment of existing and future tools. First, guided by decision analysis theory, we examine a diverse set of group decision making scenarios, characterizing variations in problem formulation, analysis goals, and situational features. Second, we amalgamate these findings into data and task abstractions that can be used to relate specific scenarios to the language of visualization. Finally, we use this framework to assess existing preference visualization tools in order to shed light on areas for future work in supporting group decision making.

Patrick Huber, Wen Xiao, Giuseppe Carenini. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Previous work indicates that discourse information benefits summarization.In this paper, we explore whether this synergy between discourse and summarization is bidirectional, by inferring document-level discourse trees from pre-trained neural summarizers.In particular, we generate unlabeled RST-style discourse trees from the self-attention matrices of the transformer model.Experiments across models and datasets reveal that the summarizer learns both, dependency-and constituencystyle discourse information, which is typically encoded in a single head, covering long-and short-distance discourse dependencies.Overall, the experimental results suggest that the learned discourse information is general and transferable inter-domain 1 .

Dialogue topic segmentation is critical in several dialogue modeling problems.However, popular unsupervised approaches only exploit surface features in assessing topical coherence among utterances.In this work, we address this limitation by leveraging supervisory signals from the utterance-pair coherence scoring task.First, we present a simple yet effective strategy to generate a training corpus for utterance-pair coherence scoring.Then, we train a BERT-based neural utterance-pair coherence model with the obtained training corpus.Finally, such model is used to measure the topical relevance between utterances, acting as the basis of the segmentation inference 1 .Experiments on three public datasets in English and Chinese demonstrate that our proposal outperforms the state-of-the-art baselines.

Group decision making is when two or more individuals must collectively choose among a competing set of alternatives based on their individual preferences. In these situations, it can be helpful for decision makers to model and visually compare their preferences in order to better understand each others’ points of view. Although a number of tools for preference modelling and inspection exist, none are based on detailed data and task models that capture the demands of group decision making in particular. This paper is a first step in addressing this gap. By going through the four stages of the nested model of visualization design, we have developed and tested a prototype to support group decision making when decision makers express their preferences directly on the alternatives.

The success of neural methods for image captioning suggests that similar benefits can be reaped for generating captions for information visualizations. In this preliminary study, we focus on the very popular line charts. We propose a neural model which aims to generate text from the same data used to create a line chart. Due to the lack of suitable training corpora, we collected a dataset through crowdsourcing. Experiments indicate that our model outperforms relatively simple non-neural baselines.

RST-based discourse parsing is an important NLP task with numerous downstream applications, such as summarization, machine translation and opinion mining.In this paper, we demonstrate a simple, yet highly accurate discourse parser, incorporating recent contextual language models.Our parser establishes the new state-of-the-art (SOTA) performance for predicting structure and nuclearity on two key RST datasets, RST-DT and Instr-DT.We further demonstrate that pretraining our parser on the recently available large-scale "silver-standard" discourse treebank MEGA-DT provides even larger performance benefits, suggesting a novel and promising research direction in the field of discourse analysis.

Sentiment analysis, especially for long documents, plausibly requires methods capturing complex linguistics structures.To accommodate this, we propose a novel framework to exploit task-related discourse for the task of sentiment analysis.More specifically, we are combining the largescale, sentiment-dependent MEGA-DT treebank with a novel neural architecture for sentiment prediction, based on a hybrid TreeLSTM hierarchical attention model.Experiments show that our framework using sentiment-related discourse augmentations for sentiment prediction enhances the overall performance for long documents, even beyond previous approaches using well-established discourse parsers trained on human annotated data.We show that a simple ensemble approach can further enhance performance by selectively using discourse, depending on the document length.

The lack of large and diverse discourse treebanks hinders the application of data-driven approaches, such as deep-learning, to RSTstyle discourse parsing.In this work, we present a novel scalable methodology to automatically generate discourse treebanks using distant supervision from sentiment-annotated datasets, creating and publishing MEGA-DT, a new large-scale discourse-annotated corpus.Our approach generates discourse trees incorporating structure and nuclearity for documents of arbitrary length by relying on an efficient heuristic beam-search strategy, extended with a stochastic component.Experiments on multiple datasets indicate that a discourse parser trained on our MEGA-DT treebank delivers promising inter-domain performance gains when compared to parsers trained on human-annotated discourse corpora.

Neural joint models have been shown to outperform non-joint models on several NLP and Vision tasks and constitute a thriving area of research in AI and ML. Although several researchers have worked on enhancing the interpretability of single-task neural models, in this work we present what is, to the best of our knowledge, the first interface to support the interpretation of results produced by joint models, focusing in particular on NLP settings. Our interface is intended to enhance interpretability of these models for both NLP practitioners and domain experts (e.g., linguists).

Patrick Huber, Giuseppe Carenini. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Wen Xiao, Giuseppe Carenini. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Linzi Xing, Michael J. Paul, Giuseppe Carenini. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Discourse processing is a suite of Natural Language Processing (NLP) tasks to uncover linguistic structures from texts at several levels, which can support many text mining applications. This involves identifying the topic structure, the coherence structure, the coreference structure, and the conversation structure for conversational discourse. Taken together, these structures can inform text summarization, essay scoring, sentiment analysis, machine translation, information extraction, question answering, and thread recovery. The tutorial starts with an overview of basic concepts in discourse analysis - monologue vs. conversation, synchronous vs. asynchronous conversation, and key linguistic structures in discourse analysis. It then covers traditional machine learning methods along with the most recent works using deep learning, and compare their performances on benchmark datasets. For each discourse structure we describe, we show its applications in downstream text mining tasks. Methods and metrics for evaluation are discussed in detail. We conclude the tutorial with an interactive discussion of future challenges and opportunities.

In this paper we present results from an exploratory user study to uncover which user characteristics (e.g., perceptual speed, verbal working memory, etc.) play a role in how users process textual documents with embedded visualizations (i.e., Magazine Style Narrative Visualizations). We present our findings as a step toward developing user-adaptive support, and provide suggestions on how our results can be leveraged for creating a set of meaningful interventions for future evaluation.

Thread disentanglement is a precursor to any high-level analysis of multiparticipant chats. Existing research approaches the problem by calculating the likelihood of two messages belonging in the same thread. Our approach leverages a newly annotated dataset to identify reply relationships. Furthermore, we explore the usage of an RNN, along with large quantities of unlabeled data, to learn semantic relationships between messages. Our proposed pipeline, which utilizes a reply classifier and an RNN to generate a set of disentangled threads, is novel and performs well against previous work.

Community question answering (CQA) forums can provide effective means for sharing information and addressing a user's information needs about particular topics. However, many such online forums are not moderated, resulting in many low quality and redundant comments, which makes it very challenging for users to find the appropriate answers to their questions. In this paper, we apply a user-centered design approach to develop a system, CQAVis, which supports users in identifying high quality comments and get their questions answered. Informed by the user's requirements, the system combines both text analytics and interactive visualization techniques together in a synergistic way. Given a new question posed by the user, the text analytic module automatically finds relevant answers by exploring existing related questions and the comments within their threads. Then the visualization module presents the search results to the user and supports the exploration of related comments. We have evaluated the system in the wild by deploying it within a CQA forum among thousands of real users. Through the online study, we gained deeper insights about the potential utility of the system, as well as learned generalizable lessons for designing visual text analytics systems for the domain of CQA forums.

We define and motivate the problem of summarizing partial email threads.This problem introduces the challenge of generating reference summaries for partial threads when human annotation is only available for the threads as a whole, particularly when the human-selected sentences are not uniformly distributed within the threads.We propose an oracular algorithm for generating these reference summaries with arbitrary length, and we are making the resulting dataset publicly available 1 .In addition, we apply a recent unsupervised method based on Bayesian Surprise that incorporates background knowledge into partial thread summarization, extend it with conversational features, and modify the mechanism by which it handles redundancy.Experiments with our method indicate improved performance over the baseline for shorter partial threads; and our results suggest that the potential benefits of background knowledge to partial thread summarization should be further investigated with larger datasets.

Discourse Parsing and Sentiment Analysis are two fundamental tasks in Natural Language Processing that have been shown to be mutually beneficial.In this work, we design and compare two Neural models for jointly learning both tasks.In the proposed approach, we first create a vector representation for all the text segments in the input sentence.Next, we apply three different Recursive Neural Net models: one for discourse structure prediction, one for discourse relation prediction and one for sentiment analysis.Finally, we combine these Neural Nets in two different joint models: Multi-tasking and Pre-training.Our results on two standard corpora indicate that both methods result in improvements in each task but Multi-tasking has a bigger impact than Pre-training.Specifically for Discourse Parsing, we see improvements in the prediction on the set of contrastive relations.

There is increasing evidence that the effectiveness of Information Visualization (Infovis) is affected by the user needs and abilities. For instance, cognitive abilities (e.g., perceptual speed, working memory) [e.g., 1-4] have been shown to impact users' performance and satisfaction with a given visualization. These findings suggest that it can be valuable to develop visualization systems that can provide personalized support targeting specific user characteristics. Furthermore, recent research [e.g., 3,5] has shown that eye tracking data can be leveraged to identify the elements of a visualization for which specific user differences hinder user experience or performance, thus providing concrete information on which specific personalized support could be helpful for different users (e.g., users with low perceptual speed may benefit from help in processing legends [1]). Though these findings are encouraging toward the design of user-adaptive or customized visualizations, they are generally related to either fictional tasks or research prototypes. So, it is unclear if existing results on the value of user-adaptive visualizations can transfer to real-world settings.

The ACM Conference on Intelligent User Interfaces (IUI) is the annual meeting of the intelligent user interface community and serves as a premier international forum for reporting outstanding research and development on intelligent user interfaces. ACM IUI is where the Human-Computer Interaction (HCI) community meets the Artificial Intelligence (AI) community. Here we summarize the latest trends in IUI based on our experience organizing the 20th ACM IUI Conference in Atlanta in 2015.

Discourse parsing is a popular technique widely used in text understanding, sentiment analysis and other NLP tasks. However, for most discourse parsers, the performance varies significantly across different discourse relations. In this paper, we first validate the underfitting hypothesis, i.e., the less frequent a relation is in the training data, the poorer the performance on that relation. We then explore how to increase the number of positive training instances, without resorting to manually creating additional labeled data. We propose a training data enrichment framework that relies on co-training of two different discourse parsers on unlabeled documents. Importantly, we show that co-training alone is not sufficient. The framework requires a filtering step to ensure that only “good quality” unlabeled documents can be used for enrichment and re-training. We propose and evaluate two ways to perform the filtering. The first is to use an agreement score between the two parsers. The second is to use only the confidence score of the faster parser. Our empirical results show that agreement score can help to boost the performance on infrequent relations, and that the confidence score is a viable approximation of the agreement score for infrequent relations.

Online conversations, such as blogs, provide rich amount of information and opinions about popular queries. Given a query, traditional blog sites return a set of conversations often consisting of thousands of comments with complex thread structure. Since the interfaces of these blog sites do not provide any overview of the data, it becomes very difficult for the user to explore and analyze such a large amount of conversational data. In this paper, we present MultiConVis, a visual text analytics system designed to support the exploration of a collection of online conversations. Our system tightly integrates NLP techniques for topic modeling and sentiment analysis with information visualizations, by considering the unique characteristics of online conversations. The resulting interface supports the user exploration, starting from a possibly large set of conversations, then narrowing down to the subset of conversations, and eventually drilling-down to the set of comments of one conversation. Our evaluations through case studies with domain experts and a formal user study with regular blog readers illustrate the potential benefits of our approach, when compared to a traditional blog reading interface.

Since the mid-2000s, there has been exponential growth of asynchronous online conversations, thanks to the rise of social media. Analyzing and gaining insights from such conversations can be quite challenging for a user, especially when the discussion becomes very long. A promising solution to this problem is topic modeling, since it may help the user to understand quickly what was discussed in a long conversation and to explore the comments of interest. However, the results of topic modeling can be noisy, and they may not match the user’s current information needs. To address this problem, we propose a novel topic modeling system for asynchronous conversations that revises the model on the fly on the basis of users’ feedback. We then integrate this system with interactive visualization techniques to support the user in exploring long conversations, as well as in revising the topic model when the current results are not adequate to fulfill the user’s information needs. Finally, we report on an evaluation with real users that compared the resulting system with both a traditional interface and an interactive visual interface that does not support human-in-the-loop topic modeling. Both the quantitative results and the subjective feedback from the participants illustrate the potential benefits of our interactive topic modeling approach for exploring conversations, relative to its counterparts.

This paper summarizes an ongoing multi-year project aiming to uncover knowledge and techniques for devising intelligent environments for user-adaptive visualizations. We ran three studies designed to investigate the impact of user and task characteristics on user performance and satisfaction in different visualization contexts. Eye-tracking data collected in each study was analyzed to uncover possible interactions between user/task characteristics and gaze behavior during visualization processing. Finally, we investigated user models that can assess user characteristics relevant for adaptation from eye tracking data.

In the last decade, there has been an exponential growth of asynchronous online conversations thanks to the rise of social media. Analyzing and gaining insights from such conversations can be quite challenging for a user, especially when the discussion becomes very long. A promising solution to this problem is topic modeling, since it may help the user to quickly understand what was discussed in the long conversation and explore the comments of interest. However, the results of topic modeling can be noisy and may not match the user's current information needs. To address this problem, we propose a novel topic modeling system for asynchronous conversations that revises the model on the fly based on user's feedback. We then integrate this system with interactive visualization techniques to support the user in exploring long conversations, as well as revising the topic model when the current results are not adequate to fulfill her information needs. An evaluation with real users illustrates the potential benefits of our approach for exploring conversations, when compared to both a traditional interface as well as an interactive visual interface that does not support human-in-the-loop topic model.

User performance and satisfaction when working with an interface is influenced by how quickly the user can acquire the skills necessary to work with the interface through practice. Learning curves are mathematical models that can represent a user's skill acquisition ability through parameters that describe the user's initial expertise as well as her learning rate. This information could be used by an interface to provide adaptive support to users who may otherwise be slow in learning the necessary skills. In this paper, we investigate the feasibility of predicting in real time a user's learning curve when working with ValueChart, an interactive visualization for decision making. Our models leverage various data sources (a user's gaze behavior, pupil dilation, cognitive abilities), and we show that they outperform a baseline that leverages only knowledge on user task performance so far. We also show that the best performing model achieves good accuracies in predicting users' learning curves even after observing users' performance only on a few tasks. These results are promising toward the design of user-adaptive visualizations that can dynamically support a user in acquiring the necessary skills to complete visual tasks.

Clauses and sentences rarely stand on their own in an actual discourse; rather, the relationship between them carries important information that allows the discourse to express a meaning as a whole beyond the sum of its individual parts. Rhetorical analysis seeks to uncover this coherence structure. In this article, we present CODRA— a COmplete probabilistic Discriminative framework for performing Rhetorical Analysis in accordance with Rhetorical Structure Theory, which posits a tree representation of a discourse. CODRA comprises a discourse segmenter and a discourse parser. First, the discourse segmenter, which is based on a binary classifier, identifies the elementary discourse units in a given text. Then the discourse parser builds a discourse tree by applying an optimal parsing algorithm to probabilities inferred from two Conditional Random Fields: one for intra-sentential parsing and the other for multi-sentential parsing. We present two approaches to combine these two stages of parsing effectively. By conducting a series of empirical evaluations over two different data sets, we demonstrate that CODRA significantly outperforms the state-of-the-art, often by a wide margin. We also show that a reranking of the k-best parse hypotheses generated by CODRA can potentially improve the accuracy even further.

We propose a novel abstractive querybased summarization system for conversations, where queries are defined as phrases reflecting a user information needs.We rank and extract the utterances in a conversation based on the overall content and the phrasal query information.We cluster the selected sentences based on their lexical similarity and aggregate the sentences in each cluster by means of a word graph model.We propose a ranking strategy to select the best path in the constructed graph as a query-based abstract sentence for each cluster.A resulting summary consists of abstractive sentences representing the phrasal query information and the overall content of the conversation.Automatic and manual evaluation results over meeting, chat and email conversations show that our approach significantly outperforms baselines and previous extractive models.

There is increasing evidence that the effectiveness of information visualization techniques can be impacted by the particular needs and abilities of each user. This suggests that it is important to investigate information visualization systems that can dynamically adapt to each user. In this paper, we address the question of how to adapt. In particular, we present a study to evaluate a variety of visual prompts, called "interventions", that can be performed on a visualization to help users process it. Our results show that some of the tested interventions perform better than a condition in which no intervention is provided, both in terms of task performance as well as subjective user ratings. We also discuss findings on how intervention effectiveness is influenced by individual differences and task complexity.

Casual online forums such as Reddit, Slashdot and Digg, are continuing to in-crease in popularity as a means of com-munication. Detecting disagreement in this domain is a considerable challenge. Many topics are unique to the conversa-tion on the forum, and the appearance of disagreement may be much more sub-tle than on political blogs or social me-dia sites such as twitter. In this analy-sis we present a crowd-sourced annotated corpus for topic level disagreement detec-tion in Slashdot, showing that disagree-ment detection in this domain is difficult even for humans. We then proceed to show that a new set of features determined from the rhetorical structure of the con-versation significantly improves the per-formance on disagreement detection over a baseline consisting of unigram/bigram features, discourse markers, structural fea-tures and meta-post features. 1

We propose a novel abstractive summarization system for product reviews by taking advantage of their discourse structure.First, we apply a discourse parser to each review and obtain a discourse tree representation for every review.We then modify the discourse trees such that every leaf node only contains the aspect words.Second, we aggregate the aspect discourse trees and generate a graph.We then select a subgraph representing the most important aspects and the rhetorical relations between them using a PageRank algorithm, and transform the selected subgraph into an aspect tree.Finally, we generate a natural language summary by applying a template-based NLG framework.Quantitative and qualitative analysis of the results, based on two user studies, show that our approach significantly outperforms extractive and abstractive baselines.

In this paper, we present an automatic abstractive summarization system of meeting conversations. Our system ex-tends a novel multi-sentence fusion algo-rithm in order to generate abstract tem-plates. It also leverages the relationship between summaries and their source meeting transcripts to select the best templates for generating abstractive summaries of meetings. Our manual and automatic evaluation results demonstrate the success of our system in achieving higher scores both in readability and in-formativeness. 1.

A key challenge for information visualization designers lies in developing systems that best support users in terms of their individual abilities, needs, and preferences. However, most visualizations require users to first gather a certain set of skills before they can efficiently process the displayed information. This paper presents a first step towards designing visualizations that provide personalized support in order to ease the so-called 'learning curve' during a user's skill acquisition phase. We present prediction models, trained on users' gaze data, that can identify if users are still in the skill acquisition phase or if they have gained the necessary abilities. The paper first reveals that users exhibit the learning curve even during the usage of simple information visualizations, and then shows that we can generate reasonably accurate predictions about a user's skill acquisition using solely their eye gaze behavior.

In this paper, we present a novel supervised approach to the problem of summarizing email conversations and modeling dialogue acts. We assume that there is a relationship between dialogue acts and important sen-tences. Based on this assumption, we intro-duce a sequential graphical model approach which simultaneously summarizes email conversation and models dialogue acts. We compare our model with sequential and non-sequential models, which independent-ly conduct the tasks of extractive summari-zation and dialogue act modeling. An empirical evaluation shows that our ap-proach significantly outperforms all base-lines in classifying correct summary sentences without losing performance on dialogue act modeling task. 1

Abstract There is increasing evidence that user characteristics can have a significant impact on visualization effectiveness, suggesting that visualizations could be designed to better fit each user's specific needs. Most studies to date, however, have looked at static visualizations. Studies considering interactive visualizations have only looked at a limited number of user characteristics, and consider either low‐level tasks (e.g., value retrieval), or high‐level tasks (in particular: discovery), but not both. This paper contributes to this line of work by looking at the impact of a large set of user characteristics on user performance with interactive visualizations, for both low and high‐level tasks. We focus on interactive visualizations that support decision making, exemplified by a visualization known as Value Charts. We include in the study two versions of ValueCharts that differ in terms of layout, to ascertain whether layout mediates the impact of individual differences and could be considered as a form of personalization. Our key findings are that (i) performance with low and high‐level tasks is affected by different user characteristics, and (ii) users with low visual working memory perform better with a horizontal layout. We discuss how these findings can inform the provision of personalized support to visualization processing.

Abstract Today it is quite common for people to exchange hundreds of comments in online conversations (e.g., blogs). Often, it can be very difficult to analyze and gain insights from such long conversations. To address this problem, we present a visual text analytic system that tightly integrates interactive visualization with novel text mining and summarization techniques to fulfill information needs of users in exploring conversations. At first, we perform a user requirement analysis for the domain of blog conversations to derive a set of design principles. Following these principles, we present an interface that visualizes a combination of various metadata and textual analysis results, supporting the user to interactively explore the blog conversations. We conclude with an informal user evaluation, which provides anecdotal evidence about the effectiveness of our system and directions for further design.

The drive toward sustainable wastewater management is challenging the conventional paradigm of linear end-of-pipe solutions. A shift toward more sustainable solutions requires that information about new ideas, systems, and technologies be more readily accessible for addressing wastewater problems. It is commonly argued that decision-making needs to involve engineers and other community representatives to define values and brainstorm solutions. This paper describes a decision support system (DSS) prototype that is designed to help community planners identify solutions which balance environmental, economic, and social goals. The system is designed to be scalable, adaptable, and flexible to allow fair assessment of new ideas and technologies. It supports the exploration of consequences of various alternatives and visualizes the tradeoffs between them. Our DSS takes in modular descriptions of components and a description of a community context, automates the design of alternative wastewater systems, and facilitates evaluating how well each design satisfies the given context. It provides an adaptable platform from which new solutions can be designed without having to predefine how a single component fits within a specific system. Our DSS facilitates the exploration of alternative solutions by visualizing the effect of various tradeoffs and their consequences in relation to the community's sustainability goals.

Information visualization systems have traditionally followed a one-size-fits-all model, typically ignoring an individual user's needs, abilities, and preferences. However, recent research has indicated that visualization performance could be improved by adapting aspects of the visualization to the individual user. To this end, this article presents research aimed at supporting the design of novel user-adaptive visualization systems. In particular, we discuss results on using information on user eye gaze patterns while interacting with a given visualization to predict properties of the user's visualization task; the user's performance (in terms of predicted task completion time); and the user's individual cognitive abilities, such as perceptual speed, visual working memory, and verbal working memory. We provide a detailed analysis of different eye gaze feature sets, as well as over-time accuracies. We show that these predictions are significantly better than a baseline classifier even during the early stages of visualization usage. These findings are then discussed with a view to designing visualization systems that can adapt to the individual user in real time.

There is increasing evidence that users' characteristics such as cognitive abilities and personality have an impact on the effectiveness of information visualization techniques. This paper investigates the relationship between such characteristics and fine-grained user attention patterns. In particular, we present results from an eye tracking user study involving bar graphs and radar graphs, showing that a user's cognitive abilities such as perceptual speed and verbal working memory have a significant impact on gaze behavior, both in general and in relation to task difficulty and visualization type. These results are discussed in view of our long-term goal of designing information visualisation systems that can dynamically adapt to individual user characteristics.

Information Visualization systems have traditionally followed a one-size-fits-all model, typically ignoring an individual user's needs, abilities and preferences. However, recent research has indicated that visualization performance could be improved by adapting aspects of the visualization to each individual user. To this end, this paper presents research aimed at supporting the design of novel user-adaptive visualization systems. In particular, we discuss results on using information on user eye gaze patterns while interacting with a given visualization to predict the user's visualization tasks, as well as user cognitive abilities including perceptual speed, visual working memory, and verbal working memory. We show that such predictions are significantly better than a baseline classifier even during the early stages of visualization usage. These findings are discussed in view of designing visualization systems that can adapt to each individual user in real-time.

In many decision‐making scenarios, people can benefit from knowing what other people's opinions are. As more and more evaluative documents are posted on the Web, summarizing these useful resources becomes a critical task for many organizations and individuals. This paper presents a framework for summarizing a corpus of evaluative documents about a single entity by a natural language summary. We propose two summarizers: an extractive summarizer and an abstractive one. As an additional contribution, we show how our abstractive summarizer can be modified to generate summaries tailored to a model of the user preferences that is solidly grounded in decision theory and can be effectively elicited from users. We have tested our framework in three user studies. In the first one, we compared the two summarizers. They performed equally well relative to each other quantitatively, while significantly outperforming a baseline standard approach to multidocument summarization. Trends in the results as well as qualitative comments from participants suggest that the summarizers have different strengths and weaknesses. After this initial user study, we realized that the diversity of opinions expressed in the corpus (i.e., its controversiality) might play a critical role in comparing abstraction versus extraction. To clearly pinpoint the role of controversiality, we ran a second user study in which we controlled for the degree of controversiality of the corpora that were summarized for the participants. The outcome of this study indicates that for evaluative text abstraction tends to be more effective than extraction, particularly when the corpus is controversial. In the third user study we assessed the effectiveness of our user tailoring strategy. The results of this experiment confirm that user tailored summaries are more informative than untailored ones.

Topic segmentation and labeling is often considered a prerequisite for higher-level conversation analysis and has been shown to be useful in many Natural Language Processing (NLP) applications. We present two new corpora of email and blog conversations annotated with topics, and evaluate annotator reliability for the segmentation and labeling tasks in these asynchronous conversations. We propose a complete computational framework for topic segmentation and labeling in asynchronous conversations. Our approach extends state-of-the-art methods by considering a fine-grained structure of an asynchronous conversation, along with other conversational features by applying recent graph-based methods for NLP. For topic segmentation, we propose two novel unsupervised models that exploit the fine-grained conversational structure, and a novel graph-theoretic supervised model that combines lexical, conversational and topic features. For topic labeling, we propose two novel (unsupervised) random walk models that respectively capture conversation specific clues from two different sources: the leading sentences and the fine-grained conversational structure. Empirical evaluation shows that the segmentation and the labeling performed by our best models beat the state-of-the-art, and are highly correlated with human annotations.

More and more today, people are engaging in conversations via email, blogs, discussion forums, text messaging and other social media. A person may want to archive these conversations and later retrieve information about what was discussed, or analyze a conversation in real-time. What topics are covered in these conversations? What opinions are people expressing? Have any decisions been made? Have action items been assigned? This tutorial will present various natural language processing (NLP) techniques that can help answer these questions, thus creating numerous new and valuable applications that can support people in more effectively participating in these conversation. The tutorial is based on a book that we have recently published, Methods for Mining and Summarizing Text Conversations.

Elsevier’s Scopus, the largest abstract and citation database of peer-reviewed literature. Search and access research from the science, technology, medicine, social sciences and arts and humanities fields.

Abstract In this work we investigate four subjectivity and polarity tasks on spoken and written conversations. We implement and compare several pattern-based subjectivity detection approaches, including a novel technique wherein subjective patterns are learned from both labeled and unlabeled data, using n-gram word sequences with varying levels of lexical instantiation. We compare the use of these learned patterns with an alternative approach of using a very large set of raw pattern features. We also investigate how these pattern-based approaches can be supplemented and improved with features relating to conversation structure. Experimenting with meeting speech and email threads, we find that our novel systems incorporating varying instantiation patterns and conversation features outperform state-of-the-art systems despite having no recourse to domain-specific features such as prosodic cues and email headers. In some cases, such as when working with noisy speech recognizer output, a small set of well-motivated conversation features performs as well as a very large set of raw patterns.

In this paper we describe a complete abstractive summarizer for meeting conversations, and evaluate the usefulness of the automatically generated abstracts in a browsing task. We contrast these abstracts with extracts for use in a meeting browser and investigate the effects of manual versus ASR transcripts on both summary types. Index Terms: summarization, automatic speech recognition, abstraction, extraction, evaluation

This workshop brought together researchers and practitioners from both text analytics and interactive visualization communities to explore, define, and develop intelligent visual interfaces that help enhance the consumption and quality of complex text analysis results. Using this workshop as a starting point, we aim to foster closer, interdisciplinary relationships among researchers from text analytics and interactive visualization communities, so they can combine their expertise together to better tackle the difficult problems that face the text analytics community today.

In this research we aim to detect subjective sentences in spontaneous speech and label them for polarity. We introduce a novel technique wherein subjective patterns are learned from both labeled and unlabeled data, using n-grams with varying levels of lexical instantiation. Applying this technique to meeting speech, we gain significant improvement over state-of-theart approaches and demonstrate the method’s robustness to ASR errors. We also show that coupling the pattern-based approach with structural and lexical features of meetings yields additional improvement. 1.

Written opinion on products and other entities can be important to consumers and researchers, but expensive and difficult to analyze. We present a multimedia interface designed to facilitate the analysis of opinions on multiple entities, which could be beneficial to many individuals and organizations. It integrates an information visualization and an intelligent system that selects notable comparisons in the data and summarizes them in text. This system applies a set of statistics for comparing opinions across entities. We conducted a study of our interface with 36 subjects. Subjects liked the visualization overall and our system's selections overlapped with those of subjects more than did the selections of baseline systems. Given the choice, subjects often changed their selections to be more consistent with those of our system. This suggests that system selections were valuable to them.

Many critical decisions for individuals and organizations are often framed as preferential choices: the process of selecting the best option out of a set of alternatives. This paper presents a task-based empirical evaluation of ValueCharts, a set of interactive visualization techniques to support preferential choice. The design of our study is grounded in a comprehensive task model and we measure both task performance and insights. In the experiment, we not only tested the overall usefulness and effectiveness of ValueCharts, but we also assessed the differences between two versions of ValueCharts, a horizontal and a vertical one. The outcome of our study is that ValueCharts seem very effective in supporting preferential choice and the vertical version appears to be more effective than the horizontal one.

Interactive algorithm visualizations (AVs) are powerful tools for teaching and learning concepts that are difficult to describe with static media alone. However, while countless AVs exist, their widespread adoption by the academic community has not occurred due to usability problems and mixed results of pedagogical effectiveness reported in the AV and education literature. This paper presents our experiences designing and evaluating CIspace, a set of interactive AVs for demonstrating fundamental Artificial Intelligence algorithms. In particular, we first review related work on AVs and theories of learning. Then, from this literature, we extract and compile a taxonomy of goals for designing interactive AVs that address key pedagogical and usability limitations of existing AVs. We advocate that differentiating between goals and design features that implement these goals will help designers of AVs make more informed choices, especially considering the abundance of often conflicting and inconsistent design recommendations in the AV literature. We also describe and present the results of a range of evaluations that we have conducted on CIspace that include semi-formal usability studies, usability surveys from actual students using CIspace as a course resource, and formal user studies designed to assess the pedagogical effectiveness of CIspace in terms of both knowledge gain and user preference. Our main results show that (i) studying with our interactive AVs is at least as effective at increasing student knowledge as studying with carefully designed paper-based materials; (ii) students like using our interactive AVs more than studying with the paper-based materials; (iii) students use both our interactive AVs and paper-based materials in practice although they are divided when forced to choose between them; (iv) students find our interactive AVs generally easy to use and useful. From these results, we conclude that while interactive AVs may not be universally preferred by students, it is beneficial to offer a variety of learning media to students to accommodate individual learning preferences. We hope that our experiences will be informative for other developers of interactive AVs, and encourage educators to exploit these potentially powerful resources in classrooms and other learning environments.

Accessing an ever increasing number of emails, possibly on small mobile devices, has become a major problem for many users. Email summarization is a promising way to solve this problem. In this paper, we propose a new framework for email summarization. One novelty is to use a fragment quotation graph to try to capture an email conversation. The second novelty is to use clue words to measure the importance of sentences in conversation summarization. Based on clue words and their scores, we propose a method called CWS, which is capable of producing a summary of any length as requested by the user. We provide a comprehensive comparison of CWS with various existing methods on the Enron data set. Preliminary results suggest that CWS provides better summaries than existing methods.

In previous work, we proposed ValueCharts, a set of visualizations and interactive techniques to support the inspection of linear models of preferences. We now identify the need to consider the decision process in its entirety, and to redesign ValueCharts in order to support all phases of preferential choice. In this paper, we present our task-based approach to the redesign of ValueCharts grounded in recent findings from both Decision Analysis and Information Visualization. We propose a set of domain-independent tasks for the design and evaluation of interactive visualizations for preferential choice. We use the resulting framework as a basis for an analytical evaluation of ValueCharts and alternative approaches. We conclude with a detailed discussion of the redesign of our system based on our analysis.

We present an interactive multimedia interface for automatically summarizing large corpora of evaluative text (e.g. online product reviews). We rely on existing techniques for extracting knowledge from the corpora but present a novel approach for conveying that knowledge to the user. Our system presents the extracted knowledge in a hierarchical visualization mode as well as in a natural language summary. We propose a method for reasoning about the extracted knowledge so that the natural language summary can include only the most important information from the corpus. Our approach is interactive in that it allows the user to explore in the original dataset through intuitive visual and textual methods. Results of a formative evaluation of our interface show general satisfaction among users with our approach.

This paper describes the design of the CIspace interactive visualization tools for teaching and learning Artificial Intelligence. Our approach to design is to iterate through three phases: identifying pedagogical and usability goals for supporting both educators and students, designing to achieve these goals, and then evaluating our system. We believe identifying these goals is essential in confronting the usability deficiencies and mixed results about the pedagogical effectiveness of interactive visualizations reported in the Education literature. The CIspace tools have been used and positively received in undergraduate and graduate classrooms at the University of British Columbia and internationally. We hope that our experiences can inform other developers of interactive visualizations and encourage their use in classrooms and other learning environments.

Capturing knowledge from free-form evaluative texts about an entity is a challenging task. New techniques of feature extraction, polarity determination and strength evaluation have been proposed. Feature extraction is particularly important to the task as it provides the underpinnings of the extracted knowledge. The work in this paper introduces an improved method for feature extraction that draws on an existing unsupervised method. By including user-specific prior knowledge of the evaluated entity, we turn the task of feature extraction into one of term similarity by mapping crude (learned) features into a user-defined taxonomy of the entity's features. Results show promise both in terms of the accuracy of the mapping as well as the reduction in the semantic redundancy of crude features.

The popularity of email has triggered researchers to look for ways to help users better organize the enormous amount of information stored in their email folders. One challenge that has not been studied extensively in text mining is the identification and reconstruction of hidden emails. A hidden email is an original email that has been quoted in at least one email in a folder, but does not present itself in the same folder. It may have been (un)intentionally deleted or may never have been received. The discovery and reconstruction of hidden emails is critical for many applications including email classification, summarization and forensics. This paper proposes a framework for reconstructing hidden emails using the embedded quotations found in messages further down the thread hierarchy. We evaluate the robustness and scalability of our framework by using the Enron public email corpus. Our experiments show that hidden emails exist widely in that corpus and also that our optimization techniques are effective in processing large email folders.

In this paper we propose ValueCharts, a set of visualizations and interactive techniques intended to support decision-makers in inspecting linear models of preferences and evaluation. Linear models are popular decision-making tools for individuals, groups and organizations. In Decision Analysis, they help the decision-maker analyze preferential choices under conflicting objectives. In Economics and the Social Sciences, similar models are devised to rank entities according to an evaluative index of interest. The fundamental goal of building models expressing preferences and evaluations is to help the decision-maker organize all the information relevant to a decision into a structure that can be effectively analyzed. However, as models and their domain of application grow in complexity, model analysis can become a very challenging task. We claim that ValueCharts will make the inspection and application of these models more natural and effective. We support our claim by showing how ValueCharts effectively enable a set of basic tasks that we argue are at the core of analyzing and understanding linear models of preferences and evaluation.

Current recommender systems, based on collaborative filtering, implement a rather limited model of interaction. These systems intelligently elicit information from a user only during the initial registration phase. Furthermore, users tend to collaborate only indirectly. We believe there are several unexplored opportunities in which information can be effectively elicited from users by making the underlying interaction model more conversational and collaborative. In this paper, we propose a set of techniques to intelligently select what information to elicit from the user in situations in which the user may be particularly motivated to provide such information. We argue that the resulting interaction improves the user experience. We conclude by reporting results of an offline experiment in which we compare the influence of different elicitation techniques on both the accuracy of the systems predictions and the users effort

We have developed a system that generates evaluative arguments that are tailored to the user, properly arranged and concise. We have also developed an evaluation framework in which the effectiveness of evaluative arguments can be measured with real users. This paper presents the results of a formal experiment we have performed in our framework to verify the influence of argument conciseness on argument effectiveness

We present an evaluation framework in which the effectiveness of evaluative arguments can be measured with real users.The framework is based on the task-efficacy evaluation method.An evaluative argument is presented in the context of a decision task and measures related to its effectiveness are assessed.Within this framework, we are currently running a formal experiment to verify whether argument effectiveness can be increased by tailoring the argument to the user and by varying the degree of argument conciseness.

We propose an argumentation strategy for generating evaluative arguments that can be applied in systems serving as personal assistants or advisors. By following guidelines from argumentation theory and by employing a quantitative model of the user's preferences, the strategy generates arguments that are tailored to the user, properly arranged and concise. Our proposal extends the scope of previous approaches both in terms of types of arguments generated, and in terms of compliance with principles from argumentation theory.

In this paper, we present and discuss our experience in the task of probability elicitation from experts for the purpose of belief network construction. In our study, we applied four techniques. Three of these techniques are available from the literature, whereas the fourth one is a technique that we developed by adapting a method for the assessment of preferences to the task of probability elicitation. The new technique is based on the analytic hierarchy process (AHP) proposed by Saaty (1980, 1994), and it allows for the quantitative assessment of the expert inconsistency. The method is, in our opinion, very promising and lends itself to be applied more extensively to the task of probability elicitation.

We claim that automatic multimedia presentation can be modeled by integrating two complementary approaches to automatic design: hierarchical planning to achieve communicative goals, and task-based graphic design. The interface between the two approaches is a domain and media independent layer of communicative goals and actions. A planning process decomposes domain-specific goals to domain-independent goals, which in turn are realized by media-specific techniques. One of these techniques is taskbased graphic design. We apply our approach to presenting information from large data sets using natural language and information graphics. Keywords Multimedia presentation, information seeking tasks, media allocation, information graphics, presentation planning. INTRODUCTION Understanding large data sets and explaining them to others via effective displays is a complex, laborious and time consuming activity. Analysts and other types of specialists on a daily and sometimes hourly basis explor...