The Pareto Principle Is Everywhere: Finding Informative Sentences for Opinion Summarization Through Leader Detection

Most previous works on opinion summarization focus on summarizing sentiment polarity distribution toward different aspects of an entity (e.g., battery life and screen of a mobile phone). However, users’ demand may be more beyond this kind of opinion summarization. Besides such coarse-grained summarization on aspects, one may prefer to read detailed but concise text of the opinion data for more information. In this paper, we propose a new framework for opinion summarization. Our goal is to assist users to get helpful opinion suggestions from reviews by only reading a short summary with a few informative sentences, where the quality of summary is evaluated in terms of both aspect coverage and viewpoints preservation. More specifically, we formulate the informative sentence selection problem in opinion summarization as a community leader detection problem, where a community consists of a cluster of sentences toward the same aspect of an entity and leaders can be considered as the most informative sentences of the corresponding aspect. We develop two effective algorithms to identify communities and leaders. Reviews of six products from Amazon.com are used to verify the effectiveness of our method for opinion summarization.