Recent Advances on Neural Headline Generation
Recent Advances on Neural Headline Generation作者机构：Department of Computer Science and Technology Tsinghua University Beijing 100084 China State Key Laboratory of Intelligent Technology and Systems Tsinqhua University Beijinq 100084 China Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing 100083 China Department of Computer Information Management Inner Mongolia University of Finance and Economics Hohhot 010000 China Jiangsu Collaborative Innovation Center for Language Ability Jiangsu Normal University Xuzhou 221009 China
出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)
年 卷 期：2017年第32卷第4期
页 面：768-784页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学]
基 金：Microsoft Research Asia 国家自然科学基金 supported by the National Basic Research 973 Program of China
主 题：neural network headline generation data analysis
摘 要：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.
维普期刊数据库