dc.contributor.authorMa, Zongyang
dc.contributor.authorSun, Aixin
dc.contributor.authorYuan, Quan
dc.contributor.authorCong, Gao
dc.date.accessioned2013-07-25T07:03:35Z
dc.date.available2013-07-25T07:03:35Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.citationMa, Z., Sun, A., Yuan, Q., & Cong, G. (2012). Topic-driven reader comments summarization. Proceedings of the 21st ACM international conference on Information and knowledge management.en_US
dc.identifier.urihttp://hdl.handle.net/10220/12257
dc.description.abstractReaders of a news article often read its comments contributed by other readers. By reading comments, readers obtain not only complementary information about this news article but also the opinions from other readers. However, the existing ranking mechanisms for comments (e.g., by recency or by user rating) fail to offer an overall picture of topics discussed in comments. In this paper, we first propose to study Topic-driven Reader Comments Summarization (Torcs) problem. We observe that many news articles from a news stream are related to each other; so are their comments. Hence, news articles and their associated comments provide context information for user commenting. To implicitly capture the context information, we propose two topic models to address the Torcs problem, namely, Master-Slave Topic Model (MSTM) and Extended Master-Slave Topic Model (EXTM). Both models treat a news article as a master document and each of its comments as a slave document. MSTM model constrains that the topics discussed in comments have to be derived from the commenting news article. On the other hand, EXTM model allows generating words of comments using both the topics derived from the commenting news article, and the topics derived from all comments themselves. Both models are used to group comments into topic clusters. We then use two ranking mechanisms Maximal Marginal Relevance (MMR) and Rating & Length (RL) to select a few most representative comments from each comment cluster. To evaluate the two models, we conducted experiments on 1005 Yahoo! News articles with more than one million comments. Our experimental results show that EXTM significantly outperforms MSTM by perplexity. Through a user study, we also confirm that the comment summary generated by EXTM achieves better intra-cluster topic cohesion and inter-cluster topic diversity.en_US
dc.language.isoenen_US
dc.rights© 2012 ACM.en_US
dc.titleTopic-driven reader comments summarizationen_US
dc.typeConference Paper
dc.contributor.conferenceInternational conference on Information and knowledge management (21st : 2012 : Maui, USA)en_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1145/2396761.2396798


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record