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


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
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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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    • "Recently, significant attention has been directed at detecting and tracking events as they surface in microblogs [12] [27]. Social media messages have also been used to predict the popularity aspect of news stories [3], as well as assist in their summarization [33]. Our problem statement looks at the reverse perspective by observing responses to news articles describing real-world events. "
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