The rapid pace of research publications makes it challenging for researchers to stay up to date. There is a growing need for automatically generated, concise literature reviews to help researchers quickly identify papers relevant to their interests. Prior work over the past decade has focused on summarizing individual research papers, typically in the context of citation generation, while the relationships among multiple papers have largely been overlooked. Existing approaches primarily generate standalone citation sentences without addressing the need for expository and transition sentences to explain the relationships among multiple citations. In this work, we propose a feature-based, LLM-prompting approach to generate richer citation texts and simultaneously capture the complex relationships among multiple papers. Our expert evaluation reveals a strong correlation between human preference and integrative writing styles, indicating that readers favor high-level, abstract citations with transition sentences that weave them into a coherent narrative.

Python is one of the most popular programming languages among both industry developers and malware authors. Despite demand for Python decompilers, community efforts to maintain automatic Python decompilation tools have been hindered by Python's aggressive language improvements and unstable bytecode specification. Every year, language features are added, code generation undergoes significant changes, and opcodes are added, deleted, and modified. Our research aims to integrate Natural Language Processing (NLP) techniques with classical Programming Language (PL) theory to create a Python decompiler that accomodates evolving language features and changes to the bytecode specification with minimal human maintenance effort. PyLINGUAL plugs in data-driven NLP components to a version-agnostic core to automatically absorb superficial bytecode and compiler changes, while leveraging programmatic components for abstract control flow reconstruction. To establish trust in the decompilation results, we introduce a stringent correctness measure based on “perfect decompilation”, a statically verifiable refinement of semantic equivalence. We demonstrate the efficacy of our approach with extensive real-world datasets of benign and malicious Python source code and their corresponding compiled PYC binaries. Our research makes three major contributions: (1) we present PyLINGUAL, a scalable, data-driven decompilation framework with state-of-the-art support for Python versions 3.6 through 3.12, improving the perfect decompilation rate by an average of 45% over the best results of existing decompiler across four datasets; (2) we provide a Python decompiler evaluation framework that verifies decompilation results with perfect decompilation; and (3) we launch PyLINGUAL as a public online service.

Text segmentation is the task of dividing a sequence of text elements (eg. words, sentences, or paragraphs) into meaningful chunks. Although exciting advances are being made in modern segmentation-based tasks, such as automatically generating podcast chapters, current segmentation similarity metrics share a critical weakness: they are content-agnostic. In this paper, we present a word-embedding-based metric of cross-textual cohesion based on the formal linguistic definition of cohesion and incorporate it into a new segmentation similarity metric, SED. Our similarity metric, SED, is capable of providing fine-grained segmentation similarity scoring for the 3 basic segmentation errors: transposition, insertion, and deletion, as well as mixtures of them, avoiding the limitations of traditional metrics. We discuss the benefits of SED and evaluate its alignment with human judgement for each of the 3 basic error types. We show that our metric aligns with human evaluations significantly more than traditional metrics. We briefly discuss future work, such as the integration of anaphora resolution into our cohesion-based metric, and make our code publicly available.

Knowledge-based, open-domain dialogue generation aims to build chit-chat systems that talk to humans using mined support knowledge. Many types and sources of knowledge have previously been shown to be useful as support knowledge. Even in the era of large language models, response generation grounded in knowledge retrieved from additional up-to-date sources remains a practically important approach. While prior work using single-source knowledge has shown a clear positive correlation between the performances of knowledge selection and response generation, there are no existing multi-source datasets for evaluating support knowledge retrieval. Further, prior work has assumed that the knowledge sources available at test time are the same as during training. This unrealistic assumption unnecessarily handicaps models, as new knowledge sources can become available after a model is trained. In this paper, we present a high-quality benchmark named multi-source Wizard of Wikipedia (Ms.WoW) for evaluating multi-source dialogue knowledge selection and response generation. Unlike existing datasets, it contains clean support knowledge, grounded at the utterance level and partitioned into multiple knowledge sources. We further propose a new challenge, dialogue knowledge plug-and-play, which aims to test an already trained dialogue model on using new support knowledge from previously unseen sources in a zero-shot fashion.

Abstractive citation text generation is usually framed as an infilling task, where a sequence-to-sequence model is trained to generate a citation given a reference paper and the context window around the target; the generated citation should be a brief discussion of the reference paper as it relates to the citing context. However, examining a recent LED-based citation generation system, we find that many of the generated citations are generic summaries of the reference paper’s main contribution, ignoring the citation context’s focus on a different topic. To address this problem, we propose a simple modification to the citation text generation task: the generation target is not only the citation itself, but the entire context window, including the target citation. This approach can be easily applied to any abstractive citation generation system, and our experimental results show that training in this way is preferred by human readers and allows the generation model to make use of contextual clues about what topic to discuss and what stance to take.

To convince readers of the novelty of their research paper, authors must perform a literature review and compose a coherent story that connects and relates prior works to the current work.This challenging nature of literature review writing makes automatic related work generation (RWG) academically and computationally interesting, and also makes it an excellent test bed for examining the capability of SOTA natural language processing (NLP) models.Since the initial proposal of the RWG task, its popularity has waxed and waned, following the capabilities of mainstream NLP approaches.In this work, we survey the zoo of RWG historical works, summarizing the key approaches and task definitions and discussing the ongoing challenges of RWG.

Text segmentation is a natural language processing task with popular applications, such as topic segmentation, element discourse extraction, and sentence tokenization.Much work has been done to develop accurate segmentation similarity metrics, but even the most advanced metrics used today, B, and WindowDiff, exhibit incorrect behavior due to their evaluation of boundaries in isolation.In this paper, we present a new segment-alignment based approach to segmentation similarity scoring and a new similarity metric A. We show that A does not exhibit the erratic behavior of B and WindowDiff, quantify the likelihood of B and WindowDiff misbehaving through simulation, and discuss the versatility of alignment-based approaches for segmentation similarity scoring.We make our implementation of A publicly available and encourage the community to explore more sophisticated approaches to text segmentation similarity scoring.

Academic research is an exploratory activity to discover new solutions to problems.By this nature, academic research works perform literature reviews to distinguish their novelties from prior work.In natural language processing, this literature review is usually conducted under the "Related Work" section.The task of related work generation aims to automatically generate the related work section given the rest of the research paper and a list of papers to cite.Prior work on this task has focused on the sentence as the basic unit of generation, neglecting the fact that related work sections consist of variable length text fragments derived from different information sources.As a first step toward a linguistically-motivated related work generation framework, we present a Citation Oriented Related Work Annotation (CORWA) dataset that labels different types of citation text fragments from different information sources.We train a strong baseline model that automatically tags the CORWA labels on massive unlabeled related work section texts.We further suggest a novel framework for human-in-the-loop, iterative, abstractive related work generation.

We present a monolingual alignment system for long, sentence-or clause-level alignments, and demonstrate that systems designed for word-or short phrase-based alignment are illsuited for these longer alignments.Our system is capable of aligning semantically similar spans of arbitrary length.We achieve significantly higher recall on aligning phrases of four or more words and outperform state-ofthe-art aligners on the long alignments in the MSR RTE corpus.

We present novel experiments in modeling the rise and fall of story characteristics within narrative, leading up to the Most Reportable Event (MRE), the compelling event that is the nucleus of the story.We construct a corpus of personal narratives from the bulletin board website Reddit, using the organization of Reddit content into topic-specific communities to automatically identify narratives.Leveraging the structure of Reddit comment threads, we automatically label a large dataset of narratives.We present a change-based model of narrative that tracks changes in formality, affect, and other characteristics over the course of a story, and we use this model in distant supervision and selftraining experiments that achieve significant improvements over the baselines at the task of identifying MREs.