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

arXiv: https://arxiv.org/abs/2402.06913 · HTML: https://mlsanity.com/paper/2402.06913/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 43 references that are in the map.
- First posted on arXiv 10 Feb 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [A Neural Attention Model for Abstractive Sentence Summarization](https://mlsanity.com/paper/1509.00685/) (2015)
- [News Summarization and Evaluation in the Era of GPT-3](https://mlsanity.com/paper/2209.12356/) (2022)
- [TLDR: Extreme Summarization of Scientific Documents](https://mlsanity.com/paper/2004.15011/) (2020)
- [Planning with Learned Entity Prompts for Abstractive Summarization](https://mlsanity.com/paper/2104.07606/) (2021)
- [Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward](https://mlsanity.com/paper/2005.01159/) (2020)
- [What Have We Achieved on Text Summarization?](https://mlsanity.com/paper/2010.04529/) (2020)
- [Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering](https://mlsanity.com/paper/1911.09801/) (2019)
- [Convex Aggregation for Opinion Summarization](https://mlsanity.com/paper/2104.01371/) (2021)
- [Efficient Few-Shot Fine-Tuning for Opinion Summarization](https://mlsanity.com/paper/2205.02170/) (2022)
- [COLO: A Contrastive Learning based Re-ranking Framework for One-Stage Summarization](https://mlsanity.com/paper/2209.14569/) (2022)
- [SciLit: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation](https://mlsanity.com/paper/2306.03535/) (2023)

## Built on by (most cited first, 1 of 1)

- [Accelerating Scientific Discovery with Multi-Document Summarization of Impact-Ranked Papers](https://mlsanity.com/paper/2508.03962/) (2025)

## 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