Summary of Opendebateevidence: a Massive-scale Argument Mining and Summarization Dataset, by Allen Roush et al.
OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset
by Allen Roush, Yusuf Shabazz, Arvind Balaji, Peter Zhang, Stefano Mezza, Markus Zhang, Sanjay Basu, Sriram Vishwanath, Mehdi Fatemi, Ravid Shwartz-Ziv
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces OpenDebateEvidence, a comprehensive dataset for argument mining and summarization sourced from the American Competitive Debate community. The dataset includes over 3.5 million documents with rich metadata, making it one of the most extensive collections of debate evidence. State-of-the-art large language models can be fine-tuned to perform argumentative abstractive summarization across various methods, models, and datasets. The authors aim to advance computational argumentation and support practical applications for debaters, educators, and researchers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a huge dataset for studying debates. It’s like a big library of arguments from high school and college debates. This will help computers learn to summarize debates better. People who study debate or teach it can use this data to make their work easier. The goal is to make computers understand debates better so people can use them to improve debating. |
Keywords
» Artificial intelligence » Summarization
