Learning to Summarize Web Documents Using Social Information

摘要

This paper presents a method named SoSVMRank, which integrates the social information of a Web document to generate a high-quality summarization. In order to do that, the summarization was formulated as a learning to rank task, in which the order of a sentence or comment was determined by its informative information. The informative information was measured by a set of local and social features in which the social features were exploited to support the local ones when modeling a sentence or comment. To enrich information, new features were also proposed. After ranking, top m ranked sentences and comments were selected as the summarization. Our method was extensively evaluated on two datasets. Promising results indicate that: (1) by using new features, our method achieves improvements in both ROUGE-1 and ROUGE-2 of the summarization over state-of-the-art baselines and (2) integrating social information benefits the summarization.