Knowledge-guided unsupervised rhetorical parsing for text summarization

doi:10.1016/j.is.2020.101615

	Knowledge-guided unsupervised rhetorical parsing for text summarization
	Hou, Shengluan 1,2; Lu, Ruqian 1,3,4
	2020-12-01
发表期刊	INFORMATION SYSTEMS
ISSN	0306-4379
卷号	94 页码:12
摘要	Automatic text summarization (ATS) has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale corpora. However, there is still no guarantee that the generated summaries are grammatical, concise, and convey all salient information as the original documents have. To make the summarization results more faithful, this paper presents an unsupervised approach that combines rhetorical structure theory, deep neural model, and domain knowledge concern for ATS. This architecture mainly contains three components: domain knowledge base construction based on representation learning, the attentional encoder-decoder model for rhetorical parsing, and subroutine-based model for text summarization. Domain knowledge can be effectively used for unsupervised rhetorical parsing thus rhetorical structure trees for each document can be derived. In the unsupervised rhetorical parsing module, the idea of translation was adopted to alleviate the problem of data scarcity. The subroutine-based summarization model purely depends on the derived rhetorical structure trees and can generate content-balanced results. To evaluate the summary results without golden standard, we proposed an unsupervised evaluation metric, whose hyper-parameters were tuned by supervised learning. Experimental results show that, on a large-scale Chinese dataset, our proposed approach can obtain comparable performances compared with existing methods. (C) 2020 Elsevier Ltd. All rights reserved.
关键词	Automatic text summarization Rhetorical structure theory Domain knowledge base Attentional encoder-decoder Natural language processing
DOI	10.1016/j.is.2020.101615
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2016YFB1000902] ; National Natural Science Foundation of China[61232015] ; National Natural Science Foundation of China[61472412] ; National Natural Science Foundation of China[61621003]
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:000567083300010
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：13[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/15506
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Hou, Shengluan
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Key Lab MADIS, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Hou, Shengluan,Lu, Ruqian. Knowledge-guided unsupervised rhetorical parsing for text summarization[J]. INFORMATION SYSTEMS,2020,94:12.
APA	Hou, Shengluan,&Lu, Ruqian.(2020).Knowledge-guided unsupervised rhetorical parsing for text summarization.INFORMATION SYSTEMS,94,12.
MLA	Hou, Shengluan,et al."Knowledge-guided unsupervised rhetorical parsing for text summarization".INFORMATION SYSTEMS 94(2020):12.