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Grouping sentences as better language unit for extractive text summarization

文献类型：期刊论文

作者Cao, Mengyun1,2; Zhuge, Hai1
刊名FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
出版日期2020-08-01
卷号109页码:331-359
关键词Text summarization Semantic Link Network Clustering Natural language processing
ISSN号0167-739X
DOI10.1016/j.future.2020.03.046
英文摘要Most existing methods for extractive text summarization aim to extract important sentences with statistical or linguistic techniques and concatenate these sentences as a summary. However, the extracted sentences are usually incoherent. The problem becomes worse when the source text and the summary are long and based on logical reasoning. The motivation of this paper is to answer the following two related questions: What is the best language unit for constructing a summary that is coherent and understandable? How is the extractive summarization process based on the language unit? Extracting larger language units such as a group of sentences or a paragraph is a natural way to improve the readability of summary as it is rational to assume that the original sentences within a larger language unit are coherent. This paper proposes a framework for group-based text summarization that clusters semantically related sentences into groups based on Semantic Link Network (SLN) and then ranks the groups and concatenates the top-ranked ones into a summary. A two-layer SLN model is used to generate and rank groups with semantic links including the is-part-of link, sequential link, similar-to link, and cause-effect link. The experimental results show that summaries composed by group or paragraph tend to contain more key words or phrases than summaries composed by sentences and summaries composed by groups contain more key words or phrases than those composed by paragraphs especially when the average length of source texts is from 7000 words to 17,000 words which is the usual length of scientific papers. Further, we compare seven clustering algorithms for generating groups and propose five strategies for generating groups with the four types of semantic links. (C) 2020 Elsevier B.V. All rights reserved.
资助项目National Natural Science Foundation of China[61640212] ; National Natural Science Foundation of China[61876048]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000536950900027
出版者ELSEVIER
源URL[http://119.78.100.204/handle/2XEOYT63/15267]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhuge, Hai
作者单位1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China
3.Guangzhou Univ, Sch Comp Sci & Network Engn, Guangzhou, Peoples R China
4.Aston Univ, Sch Engn & Appl Sci, Birmingham, W Midlands, England
推荐引用方式
GB/T 7714		 Cao, Mengyun,Zhuge, Hai. Grouping sentences as better language unit for extractive text summarization[J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE,2020,109:331-359.
APA Cao, Mengyun,&Zhuge, Hai.(2020).Grouping sentences as better language unit for extractive text summarization.FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE,109,331-359.
MLA Cao, Mengyun,et al."Grouping sentences as better language unit for extractive text summarization".FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 109(2020):331-359.

入库方式： OAI收割

来源：计算技术研究所

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