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arXiv:2406.14657cs.CL

OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset

Allen Roush, Yusuf Shabazz, Arvind Balaji, Peter Zhang, Stefano Mezza, Markus Zhang, Sanjay Basu, Sriram Vishwanath, Mehdi Fatemi, Ravid Shwartz-Ziv

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Abstract

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/Hellisotherpeople/OpenDebateEvidence-Anonymized

arXiv comments
Published to the 38th Conference on Neural Information Processing Systems (NeurIPS 2024) Track on Datasets and Benchmarks
Journal reference
DOI
Subjects
cs.CLComputation and Language cs.AIArtificial Intelligence cs.LGMachine Learning

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Linked by 1 identifiable commenter in 1 comment across 1 thread, from May 2025 to May 2025. Found in 1 HN submission from Dec 2024 to Dec 2024. Open submission discussions.

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    • Btw - my first author NeurIPS dataset and benchmarks paper is taking basically all the evidence that such debate community (American hs and college level policy and LD debate) produced over its recent history and making it easy for LLMs and people to consume it. They’ve been…

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