Summary of Which Side Are You On? a Multi-task Dataset For End-to-end Argument Summarisation and Evaluation, by Hao Li et al.
Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation
by Hao Li, Yuping Wu, Viktor Schlegel, Riza Batista-Navarro, Tharindu Madusanka, Iqra Zahid, Jiayan Zeng, Xiaochi Wang, Xinran He, Yizhi Li, Goran Nenadic
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 In this paper, researchers aim to develop an automated debate system that can help people create persuasive arguments. They introduce a new dataset called ArgSum, which contains 14k examples of claims with annotations for various tasks such as identifying evidence and ranking the convincingness of that evidence. The authors also evaluate multiple generative models on individual tasks and find that while they perform well individually, their end-to-end performance drops significantly when all four tasks are performed together. This challenge motivates future research on end-to-end argument mining and summarization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated debate systems can help people create persuasive arguments by synthesizing evidence-based claims. Researchers developed a new dataset called ArgSum that contains 14,000 examples of claims with annotations for various tasks. They tested multiple models on individual tasks and found that while they performed well, their performance dropped when all four tasks were combined. |
Keywords
» Artificial intelligence » Summarization
