Web 2.0 technologies have enabled more and more people to freely comment on different kinds of entities (e.g. sellers, products, services). The large scale of information poses the need and challenge of automatic summarization. In many cases, each of the user generated short comments comes with an overall rating. In this paper, we study the problem of generating a ``rated aspect summarization'' of short comments, which is a decomposed view of the overall ratings for the major aspects so that a user could gain different perspectives towards the target entity. We formally define the problem and decompose the solution into three steps. We demonstrate the effectiveness of our methods by using eBay sellers' feedback comments. We also quantitatively evaluate each step of our methods and study how human agree on such summarization task. The proposed methods are quite general and can be used to generate rated aspect summary given any collection of short comments each associated with an overall rating.