Automatic summarization of online customer reviews

Abstract

Online customer reviews offer valuable information for merchants and potential shoppers in e-Commerce and e-Business. However, even for a single product, the number of reviews often amounts to hundreds or thousands. Thus, summarization of multiple reviews is helpful to extract the important issues that merchants and customers are concerned about. Existing methods of multi-document summarization divide documents into non-overlapping clusters first and then summarize each cluster of documents individually with the assumption that each cluster discusses a single topic. When applied to summarize customer reviews, it is however difficult to determine the number of clusters without the prior domain knowledge, and moreover, topics often overlap with each other in a collection of customer reviews. In this paper, we propose a summarization approach based on the topical structure of multiple customer reviews. Instead of clustering and summarization, our approach extracts topics from a collection of reviews and further ranks the topics based on their frequency. The summary is then generated according to the ranked topics. The evaluation results showed that our approach outperformed the baseline summarization systems, i.e. Copernic summarizer and clustering-summarization, in terms of users' responsiveness.