Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2008

Abstract

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.

Keywords

Blog, Comments, Document summarization, Graph-based scoring, Tensor-based scoring

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Publication

SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval

First Page

291

Last Page

298

ISBN

9781605581644

Identifier

10.1145/1390334.1390385

Publisher

ACM

Additional URL

http://doi.org/10.1145/1390334.1390385

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