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Article type: Research Article
Authors: Chen, Shuang | Ren, Tao; * | Qv, Ying | Shi, Yang
Affiliations: Software College, Northeastern University, Hunnan District, Shenyang, China
Correspondence: [*] Corresponding author: Tao Ren, Software College, Northeastern University, No. 195 Chuangxin Road, Hunnan District, Shenyang, 11000, China. E-mail: chinarentao@163.com.
Abstract: Dealing with the explosive growth of web sources on the Internet requires the use of efficient systems. Automatic text summarization is capable of addressing this issue. Recent years have seen remarkable success in the use of graph theory on text extractive summarization. However, the understanding of why and how they perform so well is still not clear. In this paper, we intend to seek a better understanding of graph models, which can benefit from graph extractive summarization. Additionally, analysis has been performed qualitatively with the graph models in the design of recent graph extractive summarization. Based on the knowledge acquired from the survey, our work could provide more clues for future research on extractive summarization.
Keywords: Text summarization, extractive summarization, graph theory, extraction scheme, sentiment analysis
DOI: 10.3233/JIFS-220433
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7057-7065, 2022
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