Content extraction systems can automatically extract entities and relations from raw text and use the information to populate knowledge bases, potentially eliminating the need for manual data discovery and entry. Unfortunately, content extraction is not sufficiently accurate for end users who require high trust in the information uploaded to their databases, creating a need for human validation and correction of extracted content. In this article the potential influence of content extraction errors on a prototype semiautomated system that will allow a human reviewer to correct and validate extracted information before uploading it was examined, focusing on the identification and correction of precision errors. Content extraction was applied to 6 different corpora, and a Goals, Operators, Methods, and Selection rules Language (GOMSL) model was used to simulate the activities of a human using the prototype system to review extraction results, correct precision errors, ignore spurious instances, and validate information. The simulated task completion rate of the semiautomated system model was compared with that of a second GOMSL model that simulates the steps required for finding and entering information manually. Results quantify the efficiency advantage of the semiautomated workflow—estimated to be roughly 1.5 to 2 times more efficient than a manual workflow—and illustrate the value of employing multidisciplinary quantitative methods to calculate system-level measures of technology utility.

In this paper, we present a novel technique of first performing document genre identification, then utilizing the genre for producing tailored summaries based on a user's information seeking needs - genre oriented goal-focused summarization - such as a plot or opinion summary of a movie review. We create a test corpus to determine genre classification accuracy for 16 genres, and examine performance on various amounts of training data for machine learning algorithms - Random Forests, SVM light and Naïve Bayes. Results show that Random Forests outperforms SVM light and Naïve Bayes. The genre tag is used to inform a downstream summarization engine. We define types of summaries for 7 genres, create a ground truth corpus and analyze the results of genre oriented goal-focused summarization, showing that this type of user based summarization requires different algorithms than the leading sentence baseline which is known to perform well in the case of news articles.

This paper discusses passage extraction approaches to multidocument summarization that use available information about the document set as a whole and the relationships between the documents to build on single document summarization methodology.Multi-document summarization diers from single in that the issues of compression, speed, redundancy and passage selection are critical in the formation of useful summaries, as well as the user's goals in creating the summary.Our approach addresses these issues by using domain-independent techniques based mainly on fast, statistical processing, a metric for reducing redundancy and maximizing diversity in the selected passages, and a modular framework to allow easy parameterization for dierent genres, corpora characteristics and user requirements.We examined how h umans create multi-document summaries as well as the characteristics of such summaries and use these summaries to evaluate the performance of various multidocument summarization algorithms.

Article Free Access Share on Summarizing text documents: sentence selection and evaluation metrics Authors: Jade Goldstein Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PAView Profile , Mark Kantrowitz Just Research, 4616 Henry Street, Pittsburgh, PA Just Research, 4616 Henry Street, Pittsburgh, PAView Profile , Vibhu Mittal Just Research, 4616 Henry Street, Pittsburgh, PA Just Research, 4616 Henry Street, Pittsburgh, PAView Profile , Jaime Carbonell Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PAView Profile Authors Info & Claims SIGIR '99: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrievalAugust 1999 Pages 121–128https://doi.org/10.1145/312624.312665Published:01 August 1999Publication History 264citation3,037DownloadsMetricsTotal Citations264Total Downloads3,037Last 12 Months222Last 6 weeks42 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

No abstract available.

When working with large data sets, users perform three primary types of activities: data manipulation, data analysis, and data visualization. The data manipulation process involves the selection and transformation of data prior to viewing. This paper addresses user goals for this process and the interactive interface mechanisms that support them. We consider three classes of data manipulation goals: controlling the scope (selecting the desired portion of the data), selecting the focus of attention (concentrating on the attributes of data that are relevant to current analysis), and choosing the level of detail (creating and decomposing aggregates of data). We use this classification to evaluate the functionality of existing data exploration interface techniques. Based on these results, we have expanded an interface mechanism called the Aggregate Manipulator (AM) and combined it with Dynamic Query (DQ) to provide complete coverage of the data manipulation goals. We use real estate sales data to demonstrate how the AM and DQ synergistically function in our interface.

We present three novel tools for creating data graphics: (1) SageBrush, for assembling graphics from primitive objects like bars, lines and axes, (2) SageBook, for browsing previously created graphics relevant to current needs, and (3) SAGE, a knowledge-based presentation system that automatically designs graphics and also interprets a user's specifications conveyed with the other tools. The combination of these tools supports two complementary processes in a single environment: design as a constructive process of selecting and arranging graphical elements, and design as a process of browsing and customizing previous cases. SAGE enhances userdirected design by completing partial specifications, by retrieving previously created graphics based on their appearance and data content, by creating the novel displays that users specify, and by designing alternatives when users request them. Our approach was to propose interfaces employing styles of interaction that appear to support graphic design. Knowledge-based techniques were then applied to enable the interfaces and enhance their usability.