Multi-aspects Review Summarization Based on Identification of Important Opinions and their Similarity

TLDR: This paper proposes a method for multi- aspects review summarization based on evaluative sentence extraction that combines ratings of aspects, the tf -idf value, and the number of mentions with a similar topic and applies a clustering algorithm.

Abstract: The development of the Web services lets many users easily provide their opinions recently. Automatic summarization of enormous sentiments has been expected. Intuitively, we can summarize a review with traditional document summarization methods. However, such methods have not well-discussed "aspects". Basically, a review consists of sentiments with various aspects. We summarize reviews for each aspect so that the summary presents information without biasing to a specific topic. In this paper, we propose a method for multi- aspects review summarization based on evaluative sentence extraction. We handle three fea- tures; ratings of aspects, the tf -idf value, and the number of mentions with a similar topic. For estimating the number of mentions, we apply a clustering algorithm. By integrating these features, we generate a more appropriate summary. The experiment results show the effectiveness of our method.