Summarization of Scientific Paper Through Reinforcement Ranking on Semantic Link Network

doi:10.1109/ACCESS.2018.2856530

	Summarization of Scientific Paper Through Reinforcement Ranking on Semantic Link Network
	Sun, Xiaoping ; Zhuge, Hai 1
	2018
发表期刊	IEEE ACCESS
ISSN	2169-3536
卷号	6 页码:40611-40625
摘要	The semantic link network is a semantics modeling method for effective information services. This paper proposes a new text summarization approach that extracts semantic link network from scientific paper consisting of language units of different granularities as nodes and semantic links between the nodes, and then ranks the nodes to select Top-k sentences to compose summary. A set of assumptions for reinforcing representative nodes is set to reflect the core of paper. Then, semantic link networks with different types of node and links are constructed with different combinations of the assumptions. Finally, an iterative ranking algorithm is designed for calculating the weight vectors of the nodes in a converged iteration process. The iteration approximately approaches a stable weight vector of sentence nodes, which is ranked to select Top-k high-rank nodes for composing summary. We designed six types of ranking models on semantic link networks for evaluation. Both objective assessment and intuitive assessment show that ranking semantic link network of language units can significantly help identify the representative sentences. This paper not only provides a new approach to summarizing text based on the extraction of semantic links from text but also verifies the effectiveness of adopting the semantic link network in rendering the core of text. The proposed approach can be applied to implementing other summarization applications such as generating an extended abstract, the mind map, and the bulletin points for making the slides of a given paper. It can be easily extended by incorporating more semantic links to improve text summarization and other information services.
关键词	Semantics modeling natural language processing text summarization reinforcement
DOI	10.1109/ACCESS.2018.2856530
收录类别	SCI
语种	英语
资助项目	Guangzhou University
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000441868800024
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：28[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/2XEOYT63/4968
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.Guangzhou Univ, Lab Cyber Phys Social Intelligence, Guangzhou 510006, Guangdong, Peoples R China 2.Univ Chinese Acad Sci, Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc,Inst Comp Techn, Beijing 100049, Peoples R China 3.Aston Univ, Syst Analyt Res Inst, Birmingham B4 7ET, W Midlands, England
推荐引用方式 GB/T 7714	Sun, Xiaoping,Zhuge, Hai. Summarization of Scientific Paper Through Reinforcement Ranking on Semantic Link Network[J]. IEEE ACCESS,2018,6:40611-40625.
APA	Sun, Xiaoping,&Zhuge, Hai.(2018).Summarization of Scientific Paper Through Reinforcement Ranking on Semantic Link Network.IEEE ACCESS,6,40611-40625.
MLA	Sun, Xiaoping,et al."Summarization of Scientific Paper Through Reinforcement Ranking on Semantic Link Network".IEEE ACCESS 6(2018):40611-40625.