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

Shahbaz Syed, Khalid Al-Khatib, Martin Potthast

TL;DR

The paper introduces TL;DR Progress, a literature explorer for neural text summarization that combines a four-component annotation scheme with manual curation of 514 papers to enable fine-grained, facet-based retrieval. It delivers indicative summaries that integrate automatically extracted contextual factors with manually annotated facets, and employs LLMs for automatic terminology acquisition to improve recall of key concepts and acronyms. An interactive dashboard and a figure browser provide real-time, quantitative overviews of the field, supporting quick navigation and evaluation of model architectures, datasets, domains, and evaluation metrics. A small user study demonstrates the tool's utility for targeted literature reviews, while acknowledging limitations in automatic content fidelity and outlining future work to broaden domains and automation across the literature.

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, consisting of automatically extracted contextual factors, issues, and proposed solutions. The tool is available online at https://www.tldr-progress.de, a demo video at https://youtu.be/uCVRGFvXUj8

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

TL;DR

The paper introduces TL;DR Progress, a literature explorer for neural text summarization that combines a four-component annotation scheme with manual curation of 514 papers to enable fine-grained, facet-based retrieval. It delivers indicative summaries that integrate automatically extracted contextual factors with manually annotated facets, and employs LLMs for automatic terminology acquisition to improve recall of key concepts and acronyms. An interactive dashboard and a figure browser provide real-time, quantitative overviews of the field, supporting quick navigation and evaluation of model architectures, datasets, domains, and evaluation metrics. A small user study demonstrates the tool's utility for targeted literature reviews, while acknowledging limitations in automatic content fidelity and outlining future work to broaden domains and automation across the literature.

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, consisting of automatically extracted contextual factors, issues, and proposed solutions. The tool is available online at https://www.tldr-progress.de, a demo video at https://youtu.be/uCVRGFvXUj8
Paper Structure (17 sections, 4 figures, 6 tables)

This paper contains 17 sections, 4 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Annotation Scheme
  4. Webis Summarization Papers Corpus
  5. Corpus Construction
  6. Corpus Statistics
  7. Indicative Summaries of Papers
  8. Beyond Abstract as a Summary
  9. Contextual Information Extraction
  10. Automatic Terminology Acquisition
  11. Dashboard and Figure Browser
  12. Evaluation
  13. Purpose-driven User Study
  14. Results
  15. Feedback
  16. ...and 2 more sections

Figures (4)

  • Figure 1: Our annotation scheme is based on a summarization literature analysis. Its four components and their respective facets enable a fine-grained unified analysis of relevant papers. The indicative summary is automatically generated.
  • Figure 2: Indicative summary of a paper containing (1) an abstractive summary of the introduction, (2) manually annotated metadata attributes (details), (3) purpose of the summary encompassing the target audience, the downstream use, and the purpose, (4) claims and contributions of the paper.
  • Figure 3: An overview of the figure browser which contains all the tables and figures pulled from the papers, accompanied by their captions.
  • Figure 4: Evaluation results of the effectiveness and usefulness of TL;DR Progress compared to Semantic Scholar.