北京大学学报（自然科学版）2017，Vol.53Issue(2)：197-203,7.DOI:10.13209/j.0479-8023.2017.028
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
摘要
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.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Key words
query-focused summarization/semantic similarity/max-margin learning/convolutional neural network分类
信息技术与安全科学引用本文复制引用
应文豪,肖欣延,李素建,吕雅娟,穗志方..一种利用语义相似度改进问答摘要的方法[J].北京大学学报（自然科学版）,2017,53(2):197-203,7.基金项目
百度-北京大学合作项目、国家重点基础研究发展计划项目(2014CB340504)和国家自然科学基金(61273278,61375074)资助 （2014CB340504）
