Review Synthesis for Micro-Review Summarization

• Main Authors: Nguyen, Thanh-Son, LAUW, Hady W., TSAPARAS, Panayiotis
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: social context; social media; social search; Computer Sciences; Databases and Information Systems; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/2631; https://ink.library.smu.edu.sg/context/sis_research/article/3631/viewcontent/wsdm15.pdf
• Institution: Singapore Management University

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.