Abstract
Automatic summarization of reader comments in on-line news is a challenging but clearly useful task. Work to date has produced extractive summaries using well-known techniques from other areas of NLP. But do users really want these, and do they support users in realistic tasks? We specify an alternative summary type for reader comments, based on the notions of issues and viewpoints, and demonstrate our user interface to present it. An evaluation to assess how well summarization systems support users in time-limited tasks (identifying issues and characterizing opinions) gives good results for this prototype.
This research is supported by the European Union’s Seventh Framework Program project SENSEI (FP7-610916).
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Funk, A., Aker, A., Barker, E., Paramita, M.L., Hepple, M., Gaizauskas, R. (2017). The SENSEI Overview of Newspaper Readers’ Comments. In: Jose, J., et al. Advances in Information Retrieval. ECIR 2017. Lecture Notes in Computer Science(), vol 10193. Springer, Cham. https://doi.org/10.1007/978-3-319-56608-5_77
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DOI: https://doi.org/10.1007/978-3-319-56608-5_77
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