Enhancing Argument Summarization: Prioritizing Exhaustiveness in Key
  Point Generation and Introducing an Automatic Coverage Evaluation Metric
Mohammad Khosravani Chenyang Huang Amine Trabelsi
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.