Exploiting User-Generated Content to Enrich Web Document Summarization

Abstract

User-generated content such as comments or tweets (also called by social information) following a Web document provides additional information for enriching the content of an event mentioned in sentences. This paper presents a framework named SoSVMRank, which integrates the user-generated content of a Web document to generate a highquality summarization. In order to do that, the summarization was formulated as a learning to rank task, in which comments or tweets are exploited to support sentences in a mutual reinforcement fashion. To model sentence-comment (or tweet) relation, a set of local and social features are proposed. After ranking, top m ranked sentences and comments (or tweets) are selected as the summarization. To validate the efficiency of our framework, sentence and story highlight extraction tasks were taken as a case study on three datasets in two languages, English and Vietnamese. Experimental results indicate that: (i) our new features improve the summary performance of the framework in term of ROUGE-scores compared to state-of-the-art baselines and (ii) the integration of user-generated content benefits single-document summarization.