Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2011

Abstract

Topic-specific opinion summarization (TOS) plays an important role in helping users digest online opinions, which targets to extract a summary of opinion expressions specified by a query, i.e. topic-specific opinionated information (TOI). A fundamental problem in TOS is how to effectively represent the TOI of an opinion so that salient opinions can be summarized to meet user’s preference. Existing approaches for TOS are either limited by the mismatch between topic-specific information and its corresponding opinionated information or lack of ability to measure opinionated information associated with different topics, which in turn affect the performance seriously. In this paper, we represent TOI by word pair and propose a weighting scheme to measure word pair. Then, we integrate word pair into a random walk model for opinionated sentence ranking and adopt MMR method for summarization. Experimental results showed that salient opinion expressions were effectively weighted and significant improvement achieved for TOS.

Keywords

Topic-specific opinion summarization, Topic-specific opinionated information, Word pair, MMR

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the Seventh Asian Information Retrieval Societies Conference

First Page

398

Last Page

409

Identifier

10.1007/978-3-642-25631-8_36

Publisher

LNCS, Springer

City or Country

Dubai, UAE

Additional URL

https://doi.org/10.1007/978-3-642-25631-8_36

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