期刊
NEUROCOMPUTING
卷 284, 期 -, 页码 52-62出版社
ELSEVIER
DOI: 10.1016/j.neucom.2018.01.020
关键词
DQN; Extractive summarization; Hierarchical architecture; Rouge metric
资金
- National Education Information Technology Research [171140001]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据