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

Khalid Al-Khatib; Martin Potthast; Shahbaz Syed

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

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arxiv 2402.06913 v1 pith:CFHM3QEP submitted 2024-02-10 cs.CL

Shahbaz Syed , Khalid Al-Khatib , Martin Potthast This is my paper

classification cs.CL

keywords summarizationtexthttpsliteratureprogresstooladditionaddressed

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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

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Accelerating Scientific Discovery with Multi-Document Summarization of Impact-Ranked Papers
cs.DL 2025-08 conditional novelty 4.0 of 10

The authors add an LLM-powered summarization tool to the BIP! Finder search engine that generates cited, concise or review-style summaries of impact-ranked search results.

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