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Comments left by readers on Web documents contain valuable information that can be utilized in different information retrieval tasks including document search, visualization, and summarization. In this paper, we study the problem of comments-oriented document summarization and aim to summarize a Web document (e.g., a blog post) by considering not only its content, but also the comments left by its readers. We identify three relations (namely, topic, quotation, and mention) by which comments can be linked to one another, and model the relations in three graphs. The importance of each comment is then scored by: (i) graph-based method, where the three graphs are merged into a multi-relation graph; (ii) tensor-based method, where the three graphs are used to construct a 3rd-order tensor. To generate a comments-oriented summary, we extract sentences from the given Web document using either feature-biased approach or uniform-document approach. The former scores sentences to bias keywords derived from comments; while the latter scores sentences uniformly with comments. In our experiments using a set of blog posts with manually labeled sentences, our proposed summarization methods utilizing comments showed significant improvement over those not using comments. The methods using feature-biased sentence extraction approach were observed to outperform that using uniform-document approach.
Hu et al. (Sun,) studied this question.