Product Review Summarization based on Facet Identification and Sentence   Clustering
  Product review nowadays has become an important source of information, not only for customers to find opinions about products easily and share their reviews with peers, but also for product manufacturers to get feedback on their products. As the number of product reviews grows, it becomes difficult for users to search and utilize these resources in an efficient way. In this work, we build a product review summarization system that can automatically process a large collection of reviews and aggregate them to generate a concise summary. More importantly, the drawback of existing product summarization systems is that they cannot provide the underlying reasons to justify users' opinions. In our method, we solve this problem by applying clustering, prior to selecting representative candidates for summarization. 
Duy Khang Ly and Kazunari Sugiyama and Ziheng Lin and Min-Yen Kan
cs.CL cs.DL