Summarization of Scientific Paper Through Reinforcement Ranking on Semantic Link Network
文献类型：期刊论文
| 作者 | Sun, Xiaoping; Zhuge, Hai1 |
| 刊名 | IEEE ACCESS
 |
| 出版日期 | 2018 |
| 卷号 | 6页码:40611-40625 |
| 关键词 | Semantics modeling natural language processing text summarization reinforcement |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2018.2856530 |
| 英文摘要 | 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. |
| 资助项目 | Guangzhou University |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000441868800024 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/4967]  |
| 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.Aston Univ, Syst Analyt Res Inst, Birmingham B4 7ET, W Midlands, England 3.Univ Chinese Acad Sci, Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc,Inst Comp Techn, Beijing 100049, Peoples R China |
| 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. |
入库方式： OAI收割
来源：计算技术研究所
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