With the rapid growth of online review platforms such as Yelp and Glassdoor, people are now relying on customer reviews to make decisions on everything from dining to employment. One study shows that over 94% of online customers read reviews before making decisions. However, massive amounts of reviews are posted on these platforms by customers every day, which makes it difficult to find the useful opinions people are looking for. Opinion summarization techniques aim to summarize large numbers of reviews into coherent and informative summaries. However, existing opinion summarization systems often generate overly generic summaries that do not effectively characterize a certain restaurant or product. This is not preferable, as the user wants specific information about each restaurant/product in the generated summary. We developed Coop, a tool that enables us to generate more specific summaries by finding better summary vector in the latent space.