Abstract:

In search services, users can get information more conveniently by reading the succinct answers to their questions. This paper introduces a feature-based method for the query-focused summarization to extract the answer summary of a user query. A convolutional neural network (CNN) is used to learn the semantic representation of a sentence, by which the similarity between a candidate answer sentence and a user query is evaluated. The neural network is trained under the framework of max-margin learning. Experiments in Baidu Knows verify that the proposed method can generate the concise answer of a user query.

Key words: query-focused summarization, semantic similarity, max-margin learning, convolutional neural network

摘要：

在搜索引擎中对用户问题直接给出简要的答案(即答案摘要)可以帮助用户更快捷的获取信息。针对这一任务, 设计一种基于特征的答案摘要抽取方法。为了进行句子相似性的计算, 提出通过使用卷积神经网络表示句子语义和计算相似性, 同时给出基于最大间隔学习的网络训练方法。在百度知道问答语料上的实验结果表明, 所提出的答案摘要抽取方法能够生成质量良好的简短回答。与基于词袋的相似性计算相比, 使用卷积神经网络能够更好地描述句子语义, 计算问题和句子之间的相似性, 有效地改善答案摘要的质量。

关键词: 问答摘要, 语义相似度计算, 最大间隔学习, 卷积神经网络