Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2009916.2009954 | SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, |
Keywords | Field | DocType |
web document,social context information,social context summarization,social context,associated social context,online social network,proposed factor graph model,standard document,social user,user generated content,factor graph | Social environment,Data mining,Social network,Computer science,Natural language processing,Artificial intelligence,Factor graph,User-generated content,Multi-document summarization,Automatic summarization,Information retrieval,Support vector machine,Document summarization | Conference |
Citations | PageRank | References |
36 | 1.10 | 30 |
Authors | ||
6 | ||