? 基于神经网络的标题生成研究进展
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Journal of Computer Science and Technology 2017, Vol. 32 Issue (4) :768-784    DOI: 10.1007/s11390-017-1758-3
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基于神经网络的标题生成研究进展
Ayana1,2,3,4, Student Member, CCF, Shi-Qi Shen1,2,3, Student Member, CCF, Yan-Kai Lin1,2,3,5, Student Member, CCF, Cun-Chao Tu1,2,3,5, Student Member, CCF, Yu Zhao1,2,3, CCF, Zhi-Yuan Liu1,2,3,5,*, Senior Member, CCF, Mao-Song Sun1,2,3,5, Senior Member, CCF
1 Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
2 State Key Laboratory of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China;
3 Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China;
4 Department of Computer Information Management, Inner Mongolia University of Finance and Economics Hohhot 010000, China;
5 Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University, Xuzhou 221009, China
Recent Advances on Neural Headline Generation
Ayana1,2,3,4, Student Member, CCF, Shi-Qi Shen1,2,3, Student Member, CCF, Yan-Kai Lin1,2,3,5, Student Member, CCF, Cun-Chao Tu1,2,3,5, Student Member, CCF, Yu Zhao1,2,3, CCF, Zhi-Yuan Liu1,2,3,5,*, Senior Member, CCF, Mao-Song Sun1,2,3,5, Senior Member, CCF
1 Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
2 State Key Laboratory of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China;
3 Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China;
4 Department of Computer Information Management, Inner Mongolia University of Finance and Economics Hohhot 010000, China;
5 Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University, Xuzhou 221009, China

摘要
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摘要 近来,神经网络技术被应用到标题生成任务中,直接利用循环神经网络实现源文档到文档标题的映射。本文对已有方法和相关改进进行详细介绍和比较。具体来讲,对不同的编码器、解码器和训练算法对神经网络标题生成整体性能的影响进行详细对比。此外,本文对大多数现有的神经标题生成系统进行定量分析,并总结了影响标题生成系统性能的几个关键因素。同时,本文对典型的神经标题生成系统进行详细的错误分析,以获得对此类系统更进一步的了解。希望本文中列出的结果和结论有益于未来的研究工作。
关键词神经网络   标题生成   数据分析     
Abstract: Recently, neural models have been proposed for headline generation by learning to map documents to headlines with recurrent neural network. In this work, we give a detailed introduction and comparison of existing work and recent improvements in neural headline generation, with particular attention on how encoders, decoders and neural model training strategies alter the overall performance of the headline generation system. Furthermore, we perform quantitative analysis of most existing neural headline generation systems and summarize several key factors that impact the performance of headline generation systems. Meanwhile, we carry on detailed error analysis to typical neural headline generation systems in order to gain more comprehension. Our results and conclusions are hoped to benefit future research studies.
Keywordsneural network   headline generation   data analysis     
Received 2016-12-20;
本文基金:

This work is supported by the National Basic Research 973 Program of China under Grant No. 2014CB340501, the National Natural Science Foundation of China under Grant Nos. 61572273, 61532010, and Microsoft Research Asia under Grant No. FY17-RESTHEME-017.

通讯作者: Zhi-Yuan Liu     Email: liuzy@tsinghua.edu.cn
About author: Ayana is a Ph.D. student of the Department of Computer Science and Technology, Tsinghua University, Beijing. She got her B.E. degree in computer science from the College of Computer Science and Technology, Inner Mongolia University, Hohhot, in 2006, and got her M.E. degree in 2009 from the College of Computer Science and Technology, Inner Mongolia University, Hohhot. Her research interest is document summarization.
引用本文:   
Ayana, Shi-Qi Shen, Yan-Kai Lin, Cun-Chao Tu, Yu Zhao, Zhi-Yuan Liu, Mao-Song Su.基于神经网络的标题生成研究进展[J]  Journal of Computer Science and Technology , 2017,V32(4): 768-784
Ayana, Shi-Qi Shen, Yan-Kai Lin, Cun-Chao Tu, Yu Zhao, Zhi-Yuan Liu, Mao-Song Sun.Recent Advances on Neural Headline Generation[J]  Journal of Computer Science and Technology, 2017,V32(4): 768-784
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