OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset
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| Format: | Preprint |
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2024
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| author | Roush, Allen Shabazz, Yusuf Balaji, Arvind Zhang, Peter Mezza, Stefano Zhang, Markus Basu, Sanjay Vishwanath, Sriram Fatemi, Mehdi Shwartz-Ziv, Ravid |
| author_facet | Roush, Allen Shabazz, Yusuf Balaji, Arvind Zhang, Peter Mezza, Stefano Zhang, Markus Basu, Sanjay Vishwanath, Sriram Fatemi, Mehdi Shwartz-Ziv, Ravid |
| contents | We introduce OpenDebateEvidence, a comprehensive dataset for argument mining and summarization sourced from the American Competitive Debate community. This dataset includes over 3.5 million documents with rich metadata, making it one of the most extensive collections of debate evidence. OpenDebateEvidence captures the complexity of arguments in high school and college debates, providing valuable resources for training and evaluation. Our extensive experiments demonstrate the efficacy of fine-tuning state-of-the-art large language models for argumentative abstractive summarization across various methods, models, and datasets. By providing this comprehensive resource, we aim to advance computational argumentation and support practical applications for debaters, educators, and researchers. OpenDebateEvidence is publicly available to support further research and innovation in computational argumentation. Access it here: https://huggingface.co/datasets/Yusuf5/OpenCaselist |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_14657 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset Roush, Allen Shabazz, Yusuf Balaji, Arvind Zhang, Peter Mezza, Stefano Zhang, Markus Basu, Sanjay Vishwanath, Sriram Fatemi, Mehdi Shwartz-Ziv, Ravid Computation and Language Artificial Intelligence Machine Learning We introduce OpenDebateEvidence, a comprehensive dataset for argument mining and summarization sourced from the American Competitive Debate community. This dataset includes over 3.5 million documents with rich metadata, making it one of the most extensive collections of debate evidence. OpenDebateEvidence captures the complexity of arguments in high school and college debates, providing valuable resources for training and evaluation. Our extensive experiments demonstrate the efficacy of fine-tuning state-of-the-art large language models for argumentative abstractive summarization across various methods, models, and datasets. By providing this comprehensive resource, we aim to advance computational argumentation and support practical applications for debaters, educators, and researchers. OpenDebateEvidence is publicly available to support further research and innovation in computational argumentation. Access it here: https://huggingface.co/datasets/Yusuf5/OpenCaselist |
| title | OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2406.14657 |