Conference Paper

Social context summarization.

DOI: 10.1145/2009916.2009954 Conference: Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, Beijing, China, July 25-29, 2011
Source: DBLP

ABSTRACT We study a novel problem of social context summarization for Web documents. Traditional summarization research has focused on extracting informative sentences from standard documents. With the rapid growth of online social networks, abundant user generated content (e.g., comments) associated with the standard documents is available. Which parts in a document are social users really caring about? How can we generate summaries for standard documents by considering both the informativeness of sentences and interests of social users? This paper explores such an approach by modeling Web documents and social contexts into a unified framework. We propose a dual wing factor graph (DWFG) model, which utilizes the mutual reinforcement between Web documents and their associated social contexts to generate summaries. An efficient algorithm is designed to learn the proposed factor graph model.Experimental results on a Twitter data set validate the effectiveness of the proposed model. By leveraging the social context information, our approach obtains significant improvement (averagely +5.0%-17.3%) over several alternative methods (CRF, SVM, LR, PR, and DocLead) on the performance of summarization.

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Available from: Zhong Su, Aug 23, 2015
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    • "Intuitively, the more often some part of the story is tweeted, the more salient it might be. Previous work assumed that such socially focused sentences might be closely related to the reference summary [18] [3] [16]. However, there are some important questions left unanswered. "
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    • "In addition, different types of features have been used, including lexical, acoustic and structural characteristics (Xie et al., 2008; Maskey and Hirschberg, 2005). Recent works have been focused on adapting summarization to the social context, exploiting user generated contents associated with the documents (Yang et al., 2011; Hu et al., 2012). Implicit and explicit community feedback in online collaborative websites have also been leveraged to detect highlights of media assets (San Pedro et al., 2009). "
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    • "For example, in [22] the graph nodes represent single keywords, which are indexed by the HITS algorithm [20]. A similar approach has been adopted in [45] [46] to address Web page summarization driven by the user-generated content coming from social networks. Unlike all of the above-mentioned approaches , our summarizer discovers association rules from the analyzed document to also represent the correlations among multiple terms in the graph-based model. "
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