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JAIT 2024 Vol.15(1): 79-86
doi: 10.12720/jait.15.1.79-86

JASPER: Journal Article Selection Program for Non-native English Readers

Nantapong Keandoungchun 1, Jitimon Angskun 2,*, and Thara Angskun 2
1. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok 10600, Thailand
2. Institute of Digital Arts and Science, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Email: nantapong.kean@kmutt.ac.th (N.K.); jitimon@g.sut.ac.th (J.A.); angskun@sut.ac.th (T.A.)
*Corresponding author

Manuscript received March 9, 2023; revised May 8, 2023; accepted July 24, 2023; published January 18, 2024.

Abstract—Typically, reading a journal article can be time-consuming, mainly for non-native English readers, because academic writing usually uses complicated vocabulary and sentences. Therefore, this paper proposes a Journal Article Selection Program for Non-native English Readers (JASPER) for selecting journal articles from abstracts using scanning and skimming techniques. JASPER employs linear searching as a scanning technique and a novel multi-layer Latent Dirichlet Allocation (LDA) as a skimming technique. It automatically classifies journal articles into multi-layer topics and selects only articles with related topics to reduce the number of articles readers must read. JASPER is evaluated in terms of accuracy and efficiency using journal articles on Computer Science topics. It achieved an average of 82.62% of the F-measure. It can also reduce the number of journal articles by an average of 98.68%. Therefore, JASPER can practically reduce the number of journal articles for non-native English readers.
Keywords—journal article selection, Journal Article Selection Program for Non-native English Readers (JASPER), article reduction, multi-layer Latent Dirichlet Allocation (LDA), multi-layer topic modeling

Cite: Nantapong Keandoungchun, Jitimon Angskun, and Thara Angskun, "JASPER: Journal Article Selection Program for Non-native English Readers," Journal of Advances in Information Technology, Vol. 15, No. 1, pp. 79-86, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.