ArgCMV：面向大语言模型时代的论证摘要基准
ArgCMV: An Argument Summarization Benchmark for the LLM-era
AI总结：
本文针对ArgKP21数据集局限性，利用大语言模型构建了包含约12K条真实在线辩论论证的ArgCMV关键点提取基准，并通过实验现有基线和开源模型验证了其更高复杂性与研究价值。
摘要：
关键点提取是论证摘要中的一项重要任务，涉及从论证中提取高层次简短摘要。现有的关键点提取方法大多在流行的ArgKP21数据集上进行评估。在本文中，我们指出了ArgKP21数据集的一些主要局限性，并论证了需要更能代表真实人类对话的新基准。利用最先进的大语言模型（LLMs），我们构建了一个新的论证关键点提取数据集ArgCMV，该数据集包含来自真实在线人类辩论的约12K条论证，涵盖超过3K个主题。与ArgKP21相比，我们的数据集表现出更高的复杂性，例如更长的论证、共指现象、主观话语单元的更高出现率以及更广泛的主题范围。我们表明现有方法不能很好地适应ArgCMV，并通过实验现有基线和最新的开源模型提供了广泛的基准结果。这项工作为长上下文在线讨论引入了一个新颖的关键点提取数据集，为下一代大语言模型驱动的摘要研究奠定了基础。
英文摘要：
Key point extraction is an important task in argument summarization which involves extracting high-level short summaries from arguments. Existing approaches for KP extraction have been mostly evaluated on the popular ArgKP21 dataset. In this paper, we highlight some of the major limitations of the ArgKP21 dataset and demonstrate the need for new benchmarks that are more representative of actual human conversations. Using SoTA large language models (LLMs), we curate a new argument key point extraction dataset called ArgCMV comprising of around 12K arguments from actual online human debates spread across over 3K topics. Our dataset exhibits higher complexity such as longer, co-referencing arguments, higher presence of subjective discourse units, and a larger range of topics over ArgKP21. We show that existing methods do not adapt well to ArgCMV and provide extensive benchmark results by experimenting with existing baselines and latest open source models. This work introduces a novel KP extraction dataset for long-context online discussions, setting the stage for the next generation of LLM-driven summarization research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
