增强论点摘要：在关键点生成中优先考虑穷尽性并引入自动覆盖率评估指标
Enhancing Argument Summarization: Prioritizing Exhaustiveness in Key Point Generation and Introducing an Automatic Coverage Evaluation Metric
AI总结：
本文提出一种基于抽取式聚类的关键点生成方法，提高覆盖率并减少冗余，同时引入一种新的覆盖率评估指标，以解决现有指标无法区分关键点质量的问题。
摘要：
社交媒体平台的激增导致了在线辩论和论点的数量增加。因此，对此类辩论进行自动摘要方法的需求是迫切的，然而这一摘要领域的研究相对不足。关键点分析（KPA）任务将论点摘要表述为以简洁的要点式句子形式表示大量论点集合的摘要，这些句子被称为关键点。KPA的一个子任务，称为关键点生成（KPG），专注于根据给定的论点生成这些关键点。本文介绍了一种新颖的抽取式关键点生成方法，在该任务上优于以往的最先进方法。我们的方法利用基于抽取的聚类方法，生成简洁、高质量的关键点，对参考摘要的覆盖率更高，且冗余输出更少。此外，我们表明现有的摘要评估指标（如ROUGE）无法区分不同质量的关键点。为此，我们提出了一种新的评估指标，通过覆盖率来评估生成的关键点。我们的代码可以在线访问。
英文摘要：
The proliferation of social media platforms has given rise to the amount of online debates and arguments. Consequently, the need for automatic summarization methods for such debates is imperative, however this area of summarization is rather understudied. The Key Point Analysis (KPA) task formulates argument summarization as representing the summary of a large collection of arguments in the form of concise sentences in bullet-style format, called key points. A sub-task of KPA, called Key Point Generation (KPG), focuses on generating these key points given the arguments. This paper introduces a novel extractive approach for key point generation, that outperforms previous state-of-the-art methods for the task. Our method utilizes an extractive clustering based approach that offers concise, high quality generated key points with higher coverage of reference summaries, and less redundant outputs. In addition, we show that the existing evaluation metrics for summarization such as ROUGE are incapable of differentiating between generated key points of different qualities. To this end, we propose a new evaluation metric for assessing the generated key points by their coverage. Our code can be accessed online.
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。
