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.

翻译：社交媒体平台的普及催生了在线辩论和论点的激增。因此，对此类辩论进行自动摘要的方法迫在眉睫，然而这一摘要领域的研究仍相对不足。关键点分析（KPA）任务将论点摘要形式化为以简洁的要点式句子（称为关键点）表示大量论点集合的摘要。KPA的子任务——关键点生成（KPG）则专注于根据给定论点生成这些关键点。本文提出了一种新颖的抽取式关键点生成方法，其性能优于该任务现有最先进方法。该方法采用基于抽取式聚类的策略，能够生成简洁、高质量的关键点，同时具有更高的参考摘要覆盖率与更低的输出冗余性。此外，我们发现现有的摘要评估指标（如ROUGE）无法区分不同质量的关键点生成结果。为此，我们提出了一种基于覆盖率评估生成关键点的新评价指标。我们的代码可在线获取。