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Accepted 31 December 2019

Impact of Minimum Spanning Tree Algorithms on Extractive Arabic Text Summarization Approach

Reda Elbarougy, Gamal Behery, Akram El Khatib, and H.H.El Hadidi

Abstract: The purpose of this research is to investigate the impact of using Minimum Spanning Tree (MST) algorithms for improving the performance of graph-based approach for Arabic Text Summarization (ATS). The previous researches were conducted in an extractive ATS that relied on a graph approach are very limited, and their performance is still low. This low performance is attributed to the characteristics of Arabic language which is morphologically complex, moreover, there is a lack in ATS researches using graph-based technique. The final results of the graph-based technique mainly rely on the weights between sentences as major features which are poorly calculated. To achieve this goal and overcome the above limitations, this paper proposed three MST algorithms; Prim’s, Kruskal’s and Boruvka’s algorithms. To investigate which MST algorithms gives the best performance in text summarization, the Essex Arabic Summaries Corpus (EASC) is used as a standard evaluation. Kruskal’s MST algorithm gives the best results. Performance is improved by 15.2%, 14.3% in recall and f-measure’s higher than previous researches done in single document extractive ATS.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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