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• W2157383798 abstract "We present a novel 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 summary, we segment the complex original multi-sentence question into several sub questions and then propose a general Conditional Random Field (CRF) based 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." @default.
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• W2157383798 date "2012-07-08" @default.
• W2157383798 modified "2023-09-28" @default.
• W2157383798 title "Community Answer Summarization for Multi-Sentence Question with Group L1 Regularization" @default.
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