.png){kind=icon}
返回
Deep reinforcement learning for extractive document summarization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2018.01.020.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We present a novel extractive document summarization approach based on a Deep Q-Network (DQN), which can model salience and redundancy of sentences in the Q-value approximation and learn a policy that maximize the Rouge score with respect to gold summaries. We design two hierarchical network architectures to not only generate informative features from the document to represent the states of DQN, but also create a list of potential actions from sentences in the document for the DQN. At training time, our model is directly trained on reference summaries generated by human, eliminating the need for sentence-level extractive labels. For testing, we evaluate this model on the CNN/Daily corpus, the DUC 2002 dataset and the DUC 2004 dataset using Rouge metric. Our experiments show that our approach achieves performance which is better than or comparable to state-of-the-art models on these corpora without any access to linguistic annotation. This is the first time DQN has been applied to extractive summarization tasks. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
DQN
Extractive summarization
Hierarchical architecture
Rouge metric
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Study of bi-directional buck-boost converter topologies for application in electrical vehicle motor drives应用于电动汽车电机驱动的双向buck-boost变换器拓扑研究
PREAI
分享收藏Adaptive Dynamic Programming and Adaptive Optimal Output Regulation of Linear Systems线性系统的自适应动态规划和自适应最优输出调节
OAAI
OAAI分享收藏Output-feedback adaptive optimal control of interconnected systems based on robust adaptive dynamic programming基于鲁棒自适应动态规划的互联系统输出反馈自适应最优控制.png)
AUTOMATICAOAAI
AUTOMATICAIF5.9
OAAI分享收藏