Aspect-based extractive summarization of online reviews

Abstract

In this paper, we study the aspect-based extractive summarization based on the observations that a good summary should present representative opinions on user concerned sub-aspects within limited words. According to these observations, we argue that, two requirements, i.e. representativeness and diversity, should be considered for generating a good summary in addition to the traditional requirements of aspect-relevance and sentiment intensity. We focus on the intrinsic relationship between sentences and the dependency between extracted sentences for summarization, and thus propose a novel aspect-based summarization method for online reviews, which employs an Aspect-sensitive Markov Random Walk Model to meet the representativeness requirement, as well as a greedy redundancy removal method to meet the diversity requirement. The conducted experiments verify the effectiveness of the proposed method by comparing it with the baselines which ignores representativeness and/or diversity. The experimental results also show that, the two requirements we present are both indispensable for a good summary.