Micropinion Generation: An Unsupervised Approach to Generating Ultra-Concise Summaries of Opinions

Micropinion Generation: An Unsupervised Approach to Generating Ultra-Concise Summaries of Opinions

Ganesan, Kavita, ChengXiang Zhai, and Evelyne Viegas. “Micropinion generation: an unsupervised approach to generating ultra-concise summaries of opinions.” Proceedings of the 21st international conference on World Wide Web. ACM, 2012.

Abstract

This paper presents a new unsupervised approach to generating ultra-concise summaries of opinions. We formulate the problem of generating such a micropinion summary as an optimization problem, where we seek a set of concise and non-redundant phrases that are readable and represent key opinions in text. We measure representativeness based on a modified mutual information function and model readability with an n-gram language model. We propose some heuristic algorithms to efficiently solve this optimization problem. Evaluation results show that our unsupervised approach outperforms other state of the art summarization methods and the generated summaries are informative and readable.

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