2011b. Social Context Summarization

We study a novel problem of social context summarization for Web documents. Traditional summarization research has focused on ex-tracting 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 model-ing Web documents and social contexts into a unified framework. We propose a dual wing factor graph (DWFG) model, which uti-lizes the mutual reinforcement between Web documents and their associated social contexts to generate summaries. An efficient al-gorithm is designed to learn the proposed factor graph model. Ex-perimental 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