一种利用语义相似度改进问答摘要的方法 Improving Query-focused Summarization with CNN-based Similarity

In search services, providing simple answers to users' queries can help users to get information quickly. To deal with the task, this paper introduces a feature-based query-focused summarization method to extract the simple answer for a query. Convolutional neural network(CNN) is used to learn the semantic representation of a sentence and evaluate the similarity between a candidate answer sentence and a query. Then neural network is trained under the framework of max-margin learning. The experiments verity that our approach to query-focused summarization generates the simple versions of the answers in Baidu Knows with good quality. The substitution of the CNN-based semantic similarity for the bow-based one improves the answer summaries futher.