  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.
