As online community question answering (cQA) portals like Yahoo! Answers and Baidu Zhidao have attracted over hundreds of millions of questions, how to utilize these questions and accordant answers becomes increasingly important for cQA websites. Prior approaches focus on using information retrieval techniques to provide a ranked list of questions based on their similarities to the query. Due to the high variance of question quality and answer quality, users have to spend lots of time on finding the truly best answers from retrieved results. In this paper, we develop an answer retrieval and summarization system which directly provides an accurate and comprehensive answer summary instead of a list of similar questions to user's query. To fully explore the information of relations between queries and questions, between questions and answers, and between answers and sentences, we propose a new probabilistic scoring model to distinguish high-quality answers from low-quality answers. By fully exploiting these relations, we summarize answers using a maximum coverage model. Experiment results on the data extracted from Chinese cQA websites demonstrate the efficacy of our proposed method.
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