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Improved Text Summarization of News Articles Using GA-HC and PSO-HC
- Title
- Improved Text Summarization of News Articles Using GA-HC and PSO-HC
- Authors
- Mohsin, Muhammad; Latif, Shazad; Haneef, Muhammad; Tariq, Usman; Khan, Muhammad Attique; Kadry, Sefedine; Yong, Hwan-Seung; Choi, Jung-In
- Ewha Authors
- 용환승
- SCOPUS Author ID
- 용환승
- Issue Date
- 2021
- Journal Title
- APPLIED SCIENCES-BASEL
- ISSN
- 2076-3417
- Citation
- APPLIED SCIENCES-BASEL vol. 11, no. 22
- Keywords
- Automatic Text Summarization (ATS); genetic algorithm; Hierarchical Clustering Technique (HCT); agglomerative clustering; extracted summary; Single Document Summarization
- Publisher
- MDPI
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- Abstract
- Automatic Text Summarization (ATS) is gaining attention because a large volume of data is being generated at an exponential rate. Due to easy internet availability globally, a large amount of data is being generated from social networking websites, news websites and blog websites. Manual summarization is time consuming, and it is difficult to read and summarize a large amount of content. Automatic text summarization is the solution to deal with this problem. This study proposed two automatic text summarization models which are Genetic Algorithm with Hierarchical Clustering (GA-HC) and Particle Swarm Optimization with Hierarchical Clustering (PSO-HC). The proposed models use a word embedding model with Hierarchal Clustering Algorithm to group sentences conveying almost same meaning. Modified GA and adaptive PSO based sentence ranking models are proposed for text summary in news text documents. Simulations are conducted and compared with other understudied algorithms to evaluate the performance of proposed methodology. Simulations results validate the superior performance of the proposed methodology.
- DOI
- 10.3390/app112210511
- Appears in Collections:
- 인공지능대학 > 컴퓨터공학과 > Journal papers
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