New dataset OpenDebateEvidence advances argument mining for LLMs

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 →

• Allen Roush
• American Competitive Debate
• arXiv
• Hugging Face
• OpenDebateEvidence

• paper
• other

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