English(EN) OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset

新数据集OpenDebateEvidence推动了LLM的论点挖掘

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-04 04:00

研究人员推出了OpenDebateEvidence，这是一个用于论点挖掘和摘要的大规模数据集。该数据集源自美国辩论社区，包含超过350万份文档和丰富的元数据，使其成为计算论证的重要资源。实验表明，在该数据集上微调大型语言模型对于论证性摘要非常有效，旨在推动该领域的研发和实际应用。 AI

影响该数据集可以显著提高LLM理解和总结复杂论点的能力，造福于教育和研究领域的应用。

排序理由该集群描述了在arXiv上发布的新学术数据集和论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群描述了在arXiv上发布的新学术数据集和论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 83 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

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 · 2026-08-04 04:00

OpenDebateEvidence: 一个大规模论证挖掘和摘要数据集

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…