Understanding Human Preferences for Summary Designs in Online Debates Domain

Nattapong Sanchan, Kalina Bontcheva, Ahmet Aker


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 online channels. 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.


Summary design; automatic summarization; summary representation; text mining; information extraction

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