  Opinion summarization from online product reviews is a challenging task,
which involves identifying opinions related to various aspects of the product
being reviewed. While previous works require additional human effort to
identify relevant aspects, we instead apply domain knowledge from external
sources to automatically achieve the same goal. This work proposes AspMem, a
generative method that contains an array of memory cells to store
aspect-related knowledge. This explicit memory can help obtain a better opinion
representation and infer the aspect information more precisely. We evaluate
this method on both aspect identification and opinion summarization tasks. Our
experiments show that AspMem outperforms the state-of-the-art methods even
though, unlike the baselines, it does not rely on human supervision which is
carefully handcrafted for the given tasks.
