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.

社交媒体平台的扩散导致了在线辩论和争论的增加，因此迫切需要针对此类辩论的自动摘要方法，然而该领域的研究相对较少。本文介绍了一种新颖的针对关键点生成的抽取性方法，该方法在该任务中优于先前的最先进方法。我们的方法利用一种基于抽取聚类的方法生成简练、高质量的关键点，具有更高的参考摘要覆盖率和较少的冗余输出。此外，我们表明现有的 ROUGE 等摘要评估指标无法区分不同质量的生成关键点，为此，我们提出了一种用于评估生成关键点覆盖率的新评估指标。我们的代码可以在线访问。

提升论点摘要：在关键点生成中优先考虑全面性并引入自动覆盖评估指标

Enhancing Argument Summarization: Prioritizing Exhaustiveness in Key  Point Generation and Introducing an Automatic Coverage Evaluation Metric

Automatic evaluation metrics have been facilitating the rapid development of
automatic summarization methods by providing instant and fair assessments of
the quality of summaries. Most metrics have been developed for the general
domain, especially news and meeting notes, or other language-generation tasks.
However, these metrics are applied to evaluate summarization systems in
different domains, such as biomedical question summarization. To better
understand whether commonly used evaluation metrics are capable of evaluating
automatic summarization in the biomedical domain, we conduct human evaluations
of summarization quality from four different aspects of a biomedical question
summarization task. Based on human judgments, we identify different noteworthy
features for current automatic metrics and summarization systems as well. We
also release a dataset of our human annotations to aid the research of
summarization evaluation metrics in the biomedical domain.

本研究针对生物医学领域的问答摘要任务，从四个不同角度进行了人工评估，用以评估现有的自动化摘要评估指标和系统的可行性，并公布了人工注释数据集，以促进生物医学领域的摘要评估方法的研究。