Community Answer Summarization for Multi-Sentence Question with Group L1 Regularization

Wen Chan, Xiangdong Zhou, Wei Wang, Tat-Seng Chua

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.