Conference Proceeding Article
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.
Computer Sciences | Databases and Information Systems | Social Media
Data Management and Analytics
WSDM '15: Proceedings of the 8th ACM International Conference on Web Search and Data Mining: 31 January-6 February 2015, Shanghai
City or Country
Nguyen, Thanh-Son; LAUW, Hady Wirawan; and TSAPARAS, Panayiotis.
Review Synthesis for Micro-Review Summarization. (2015). WSDM '15: Proceedings of the 8th ACM International Conference on Web Search and Data Mining: 31 January-6 February 2015, Shanghai. 169-178. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2631