Automatic summarization of online debates

Debate summarization is one of the novel and challenging research areas in automatic text summarization which has been largely unexplored. In this paper, we develop a debate summarization pipeline to summarize key topics which are discussed or argued in the two opposing sides of online debates. We view that the generation of debate summaries can be achieved by clustering, cluster labeling, and visualization. In our work, we investigate two different clustering approaches for the generation of the summaries. In the first approach, we generate the summaries by applying purely term-based clustering and cluster labeling. The second approach makes use of X-means for clustering and Mutual Information for labeling the clusters. Both approaches are driven by ontologies. We visualize the results using bar charts. We think that our results are a smooth entry for users aiming to receive the first impression about what is discussed within a debate topic containing waste number of argumentations

Understanding Human Preferences for Summary Designs in Online Debates Domain

Research on automatic text summarization has primarily focused on summarizing news, web pages, scientific papers, etc. While in some of these text genres, it is intuitively clear what constitutes a good summary, the issue is much less clear cut in social media scenarios like online debates, product reviews, etc., where summaries can be presented in many ways. As yet, there is no analysis about which summary representation is favored by readers. In this work, we empirically analyze this question and elicit readers’ preferences for the different designs of summaries for online debates. Seven possible summary designs in total were presented to 60 participants via an online study. Participants were asked to read and assign preference scores to each summary design. The results indicated that the combination of Chart Summary and Side-By-Side Summary is the most preferred summary design. This finding is important for future work in automatic text summarization of online debates