CLFeb 10, 2024

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

arXiv:2402.06913v1104 citationsh-index: 14Has CodeEACL
Originality Synthesis-oriented
AI Analysis

This tool aids researchers in navigating the literature on text summarization, but it is incremental as it builds on existing annotation and search methods.

The authors introduced TL;DR Progress, a tool for exploring 514 papers on neural text summarization by organizing them with a comprehensive annotation scheme and enabling fine-grained search, including manual annotations and automatically generated summaries.

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

Code Implementations1 repo
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The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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