Publication Type

Conference Proceeding Article

Publication Date

2-2015

Abstract

Micro-reviews is a new type of user-generated content arising from the prevalence of mobile devices and social media in the past few years. Micro-reviews are bite-size reviews (usually under 200 characters), commonly posted on social media or check-in services, using a mobile device. They capture the immediate reaction of users, and they are rich in information, concise, and to the point. However, the abundance of micro-reviews, and their telegraphic nature make it increasingly difficult to go through them and extract the useful information, especially on a mobile device. In this paper, we address the problem of summarizing the micro-reviews of an entity, such that the summary is representative, compact, and readable. We formulate the summarization problem as that of synthesizing a new "review" using snippets of full-text reviews. To produce a summary that naturally balances compactness and representativeness, we work within the Minimum Description Length framework. We show that finding the optimal summary is NP-hard, and we consider approximation and heuristic algorithms. We perform a thorough evaluation of our methodology on real-life data collected from Foursquare and Yelp. We demonstrate that our summaries outperform individual reviews, as well as existing summarization approaches.

Discipline

Computer Sciences | Databases and Information Systems | Social Media

Research Areas

Data Management and Analytics

Publication

WSDM '15: Proceedings of the 8th ACM International Conference on Web Search and Data Mining: 31 January-6 February 2015, Shanghai

First Page

169

Last Page

178

ISBN

9781450333177

Identifier

10.1145/2684822.2685321

Publisher

ACM

City or Country

New York

Additional URL

http://dx.doi.org/10.1145/2684822.2685321

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