There is a great need for effective summarization methods to absorb the key points of large amounts of opinions expressed on the Web. In this paper, we study the problem of opinionated text summarization, which aims to generate a coherent summary for a set of opinionated texts towards a specific topic (e.g., a movie or a controversial issue). The main characteristic of this problem is that the input set contains an arbitrary number of texts, which brings about redundant opinions and useless texts. Further, informative opinions to be summarized are scattered over different opinionated texts, thus it is vital to avoid focusing only on partial opinions. However, previous work can not tackle the above two issues effectively. To address such issues, we propose a two-stage graph-to-sequence learning framework for summarizing opinionated texts. The first stage selects summary-worthy texts from all input opinionated texts, and we construct an opinion relation graph to help estimate salience via exploiting the relationships among the input texts. Given the selected texts, the second stage generates an opinion summary via a maximal marginal relevance guided graph-to-sequence model, which gives consideration to both salient and non-redundant opinions. Experimental results on two benchmark datasets show that our framework outperforms the existing state-of-the-art methods. Human evaluation further verifies that our framework can generate more informative and compact opinion summaries than previous methods.