TL;DR Progress: Multi-faceted Literature Exploration in Text Summarization
Syed, Shahbaz, Al-Khatib, Khalid, Potthast, Martin
–arXiv.org Artificial Intelligence
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
arXiv.org Artificial Intelligence
Feb-10-2024
- Country:
- Oceania > Australia (0.04)
- North America
- United States
- Washington > King County
- Seattle (0.04)
- New York > New York County
- New York City (0.04)
- New Jersey > Essex County
- Newark (0.04)
- California > Los Angeles County
- Long Beach (0.04)
- Washington > King County
- Canada
- United States
- Europe
- Asia > China
- Hong Kong (0.04)
- Genre:
- Research Report (1.00)
- Overview (0.69)
- Technology: