TL;DR Progress: Multi-faceted Literature Exploration in Text Summarization
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Abstract (arXiv)
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
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 43 references that are in the map. First posted on arXiv 10 Feb 2024.
Builds on
15 of 43 references.
References · 15
- 2017Attention Is All You Needcited by 59,932
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
- 2015A Neural Attention Model for Abstractive Sentence Summarizationcited by 394
- 2022News Summarization and Evaluation in the Era of GPT-3cited by 324
- 2020TLDR: Extreme Summarization of Scientific Documentscited by 136
- 2021Planning with Learned Entity Prompts for Abstractive Summarizationcited by 75
- 2020Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Rewardcited by 66
- 2020What Have We Achieved on Text Summarization?cited by 65
- 2019Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answeringcited by 25
- 2021Convex Aggregation for Opinion Summarizationcited by 21
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Citing papers per year: 2025: 1.
1 paper.