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New dataset OpenDebateEvidence advances argument mining for LLMs

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced OpenDebateEvidence, a large-scale dataset designed for argument mining and summarization. This dataset, derived from the American Competitive Debate community, contains over 3.5 million documents and rich metadata, making it a significant resource for computational argumentation. Experiments show that fine-tuning large language models on this dataset is effective for argumentative abstractive summarization, aiming to advance research and practical applications in the field. AI

IMPACT This dataset could significantly improve the capabilities of LLMs in understanding and summarizing complex arguments, benefiting applications in education and research.

RANK_REASON The cluster describes a new academic dataset and paper released on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset OpenDebateEvidence advances argument mining for LLMs

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The cluster describes a new academic dataset and paper released on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Allen Roush, Yusuf Shabazz, Arvind Balaji, Peter Zhang, Stefano Mezza, Markus Zhang, Sanjay Basu, Sriram Vishwanath, Mehdi Fatemi, Ravid Shwartz-Ziv ·

    OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset

    arXiv:2406.14657v4 Announce Type: replace 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 i…

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