? Recent Advances on Neural Headline Generation
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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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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

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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.
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Keywordsneural network   headline generation   data analysis     
Received 2016-12-20;
Fund:

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.

Corresponding Authors: Zhi-Yuan Liu     Email: liuzy@tsinghua.edu.cn
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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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