Exploiting User Comments for Document Summarization with Matrix Factorization

Social media presents a new method for readers who can freely discuss the content of an event mentioned in a Web document by posting relevant comments. The comments provide additional information which can be used to enrich the information of the main document. This paper introduces a new model which integrates user comments into the summarization process. While prior methods consider the same topic number between sentences and comments of a document, we argue that sentences and comments should own their different topics and they also share common hidden topics in term of same or inferred words. From this, we define a new objective function which jointly combines sentences and comments to achieve global optimization. The objective function is optimized by our non-negative matrix factorization algorithm to find out weights of sentence-matrix and comment-matrix for ranking sentences and comments. Experimental results on two datasets in English and Vietnamese show that our model achieves promising results for single-document summarization.