In this Web 2.0 era, there is an ever increasing number of product or service reviews, which must be summarized to help consumers effortlessly make informed decisions. Previous work on reviews summarization has simplified the problem by assuming that features (e.g., "display") are independent of each other and that the opinion for each feature in a review is Boolean: positive or negative. However, in reality features may be interrelated – e.g., "display" and "display color" – and the sentiment takes values in a continuous range – e.g., somewhat vs very positive. We present a novel review summarization framework that advances the state-of-the-art by leveraging a domain hierarchy of concepts to handle the semantic overlap among the features, and by accounting for different sentiment levels. We show that the problem is NP-hard and present bounded approximate algorithms to compute the most representative set of sentences, based on a principled opinion coverage framework. We experimentally evaluate the quality of the summaries using both intuitive coverage measure and a user study.