Comments-Oriented Document Summarization Based on Multi-aspect Co-feedback Ranking

With the popularity of Web 2.0, comments left by readers on web documents have drawn much attention. In this paper, we study the problem of comments-oriented document summarization, which aims to summarize a web document by considering not only its content but also the comments. Generally, most of the comments usually convey one or a few aspects of the document. Given a sentence set from both the web document and its corresponding comments to summarize, we can divide different sentences into different clusters (named “aspects”) according to the content. It is challenging and interesting to summarize the web document based on these clusters. Motivated by this, we propose a novel model: MultiAspectCoRank, for comments-oriented document summarization. Firstly we rank all the sentences based on the multiple aspects obtained from the whole document, and then provide each ranking list as feedback to others until the top-N results of each ranking list are unchanged. We get the final result by integrating these different ranking lists together. Experimental results on a set of real-world blog data with manually labeled sentences show the promising performance of our