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We present a novel answer summarization method for community Question Answering services (cQAs) to address the problem of “incomplete answer”, i.e., the “best answer” of a complex multi-sentence question misses valuable information that is contained in other answers. In order to automatically generate a novel and non-redundant community answer summary, we segment the complex original multi-sentence question into several sub questions and then propose a general Conditional Random Field (CRF) based answer summary method with group L1 regularization | Community Answer Summarization for Multi-Sentence Question with Group L1 Regularization Wen Chanf Xiangdong Zhouf Wei Wangf Tat-Seng Chuaị tSchool of Computer Science Fudan University Shanghai 200433 China 11110240007 xdzhou weiwang1 @fudan.edu.cn ị School of Computing National University of Singapore chuats@nus.edu.sg Abstract We present a novel answer summarization method for community Question Answering services cQAs to address the problem of incomplete answer i.e. the best answer of a complex multi-sentence question misses valuable information that is contained in other answers. In order to automatically generate a novel and non-redundant community answer summary we segment the complex original multi-sentence question into several sub questions and then propose a general Conditional Random Field CRF based answer summary method with group L1 regularization. Various textual and non-textual QA features are explored. Specifically we explore four different types of contextual factors namely the information novelty and non-redundancy modeling for local and non-local sentence interactions under question segmentation. To further unleash the potential of the abundant cQA features we introduce the group L1 regularization for feature learning. Experimental results on a Yahoo Answers dataset show that our proposed method significantly outperforms state-of-the-art methods on cQA summarization task. 1 Introduction Community Question and Answering services cQAs have become valuable resources for users to pose questions of their interests and share their knowledge by providing answers to questions. They perform much better than the traditional frequently asked questions FAQ systems Jijkoun and Rijke 2005 Riezler et al. 2007 which are just based 582 on natural language processing and information retrieving technologies due to the need for human intelligence in user generated contents Gyongyi et al. 2007 . In cQAs such as Yahoo Answers a resolved question often gets more than .
