Community-based Question Answering (CQA) summarization aims to generate a summary from a collection of QA pairs about a specific entity. Unlike well-structured texts such as dialogues, a set of QA pairs often contains significant redundancy, including repetitive questions and similar answers. The above property of QA pairs makes it difficult for abstractive summarizers to concentrate on salient pieces, resulting in duplication of content and omission of important information. In this paper, we propose a Focus Rectification SUMmarizer (FRSum), which employs different strategies at both the sentence level and token level to rectify the focus on representative QA pairs and distinctive tokens. The experimental results on the COQASUM dataset show that our model can generate concise and informative summaries, outperforming state-of-the-art baselines in automatic and human evaluations.