In the last years, the opinion summarization task has gained much importance because of the large amount of online information and the increasing interest in learning the user evaluation about products, services, companies, and people. Although there are many works in this area, there is room for improvement, as the results are far from ideal. In this paper, we present our investigations to generate extractive and abstractive summaries of opinions. We study some well-known methods in the area and compare them. Besides using these methods, we also develop new methods that consider the main advantages of the ones before. We evaluate them according to three traditional summarization evaluation measures: informativeness, linguistic quality, and utility of the summary. We show that we produce interesting results and that our methods outperform some methods from literature.