TL ; DR Progress: Multi-faceted Literature Exploration in Text Summarization (2024)

Abstract: This paper presents TL;DR Progress, a new tool for exploring the literature on neural text summarization. It organizes 514~papers based on a comprehensive annotation scheme for text summarization approaches and enables fine-grained, faceted search. Each paper was manually annotated to capture aspects such as evaluation metrics, quality dimensions, learning paradigms, challenges addressed, datasets, and document domains. In addition, a succinct indicative summary is provided for each paper, describing contextual factors, issues, and proposed solutions. The tool is available at {url{https://www.tldr-progress.de}}, a demo video at {url{https://youtu.be/uCVRGFvXUj8}}

## introduction research in the field of neural text summarization has evolved rapidly from the introduction of sequence-to-sequence @xcite models to the era of transformers @xcite , greatly improving our ability to produce high-quality summaries in line with human preferences @xcite . as a result, the original focus of summarization research on the news domain has broadened to various other domains such as meetings, scientific papers, scripts and opinions. to keep abreast of current advances, especially researchers new to the field must perform various tasks, including assimilating, organizing, annotating, and reviewing papers across multiple venues. although search engines tailored to scholarly documents, such as google scholar, semantic scholar, dblp, and the acl anthology, provide access to a vast collection of articles, they merely support the discovery of relevant papers from within a multidomain collection and do not (strongly) support an in-depth comparative paper analysis. this paper introduces tl;dr progress, a literature explorer designed specifically for the text summarization literature. it contributes an intuitive annotation scheme designed to streamline finegrained, facet-based systematic reviews (figure 1 and section 3). to demonstrate the capabilities of our tool, we manually analyze a collection of 514 summarization papers and cover various important aspects for an efficient literature search (section 4). as part of its goal to organize summarization research, tl;dr progress provides an indicative summary for each paper, seamlessly integrating automatically extracted contextual information with manually annotated facets (section 5). this includes identifying for what practical purpose a summarization approach is intended, the current challenges associated with summary generation, and a paper's contributions. our tool also demonstrates the practical application of large language models (llms) in automatic terminology acquisition, involving the extraction of technical terms from papers, including glossary definitions for general concepts and acronym-expansion pairs to improve researchers' recall of specific papers (section 6). foot_0 tl;dr progress has a dual function: it provides insights into current research and serves as a basis for future automation. in particular, our literature explorer shows a way forward for future research on large-scale systematic reviews of the nlp literature by extensively leveraging llms. ## related work paper aggregators such as google scholar, semantic scholar, dblp, and the acl anthology provide access to a large number of papers from different disciplines and, most importantly, facilitate their discovery. however, these platforms lack solid support for in-depth comparative analysis. per-slearn @xcite introduces a perspectivebased approach to exploring scientific literature. it empowers early career researchers to develop their viewpoints by interacting with a prompt-based model. the tool identifies evidence from relevant papers that relate to the given seed perspectives and summarizes them to make new connections. in contrast, tl;dr progress focuses on summarization and provides fine-grained facets for each paper to enhance understanding of their contributions and content, along with indicative summaries, a feature not present in perslearn. as for annotating papers, autodive @xcite automates the in-place annotation of entities and relationships in pdfs, using external domainspecific ner models. in contrast, our approach includes a domain-specific annotation scheme and manual annotation for quality assurance. in addition, our tool facilitates unsupervised automatic terminology acquisition using llms. scilit @xcite recommends relevant articles based on keywords entered by the user and generates citation sets with extracted highlights. while our tool supports keyword-based lexical search, it is less reliant on user-defined keywords due to its facet-based retrieval system. paperswithcode 2 is a platform that links papers with their code implementations. it provides an overview of the state-of-the-art in various nlp tasks. tl;dr progress complements paperswithcode (for the summarization task) by providing an interactive dashboard presenting relevant statistics (section 7) for a comprehensive understanding of the state-of-the-art. ## annotation scheme to create a comprehensive annotation scheme for summarization papers, we performed an in-depth analysis of the recent relevant literature. as shown in figure 1 , this scheme encapsulates the basic components of a neural summarization architecture, laying the foundation for a fine-grained annotation 2 https://paperswithcode.com/ tailored to individual contributions. its underlying principle is to categorize contributions according to their main focus, as papers often address one or more components within the summarization pipeline. the annotation scheme distinguishes four components: these components encompass different facets. for example, document representation includes "input encoding", "unit relationship", "data augmentation", and "external knowledge". definitions for each facet are given in ## webis summarization papers corpus to create tl;dr progress, we compiled a corpus of research papers on neural text summarization, annotated it according to our scheme, and analyzed the distribution of the papers across different dimensions. ## indicative summaries of papers in contrast to informative summaries that aim to replace the entire paper, our tool provides indicative summaries that help users quickly decide if a paper is relevant to their information need. our indicative summaries are unique in that they encompass an abstractive summary of the paper as well as multiple facets such as datasets, domains, evaluation metrics alongside other information. ## automatic terminology acquisition scientific terminology plays a vital role in research, requiring researchers to recall papers related to specific concepts or acronyms representing models/metrics. moreover, previously defined terminology might be directly referenced in subsequent papers without detailed explanation @xcite 4 https://platform.openai.com/docs/models/gpt-3-5 5 we clustered the contextual embeddings @xcite combined with dimensionality reduction using umap @xcite . ## evaluation we conducted an empirical evaluation of the tool's efficacy in supporting systematic literature reviews for text summarization. the study involved presenting targeted inquiries relevant to beginners in the field and instructing participants to leverage both tl;dr progress and semantic scholar for retrieving relevant papers. additionally, we systematically gathered feedback on the tool's usability and utility for understanding the effectiveness of its features. ## conclusion in summary, tl;dr progress offers an interactive platform for nuanced exploration of over 500 neural text summarization papers from top venues. utilizing a tailored annotation scheme, the tool guides users through multifaceted retrieval, provides insightful indicative summaries, outlines challenges, and presents a quantitative overview, easing the entry for newcomers into the field.