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

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。

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.