COMPARISON OF LEXRANK ALGORITHM AND MAXIMUM MARGINAL RELEVANCE IN SUMMARY OF INDONESIAN NEWS TEXT IN ONLINE NEWS PORTALS

  • Siti Tuhpatussania (1*) Universitas AMIKOM Yogyakarta
  • Ema Utami (2) Universitas AMIKOM Yogyakarta
  • Anggit Dwi Hartanto (3) Universitas AMIKOM Yogyakarta

    • (*) Corresponding Author
DOI: https://doi.org/10.33480/pilar.v18i2.3190

Keywords: autotext summarization, lexrank, Maximum Marginal Relevance, TF-IDF

Abstract

The presence of online media has shifted print media for news readers to get information that is fast, accurate, and easy to access. However, the problem arises because the length of the news text makes the reader bored to search for the news as a whole so the news that is obtained will be less accurate. For this reason, it is necessary to have an automatic text summary that was raised in this study as well as to compare the Maximum Marginal Relevance (MMR) algorithm and the LexRank algorithm to the summary of Indonesian news texts on the online news portal graphanews. com. the results of the comparison test of text summarization using f-measure , precision and recall show the performance of text summarization with the MMR algorithm is better where f-measure is 91.65%, precision is 91.08% and recall is 92.23%.

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Published
2022-09-13
How to Cite
Tuhpatussania, S., Utami, E., & Hartanto, A. (2022). COMPARISON OF LEXRANK ALGORITHM AND MAXIMUM MARGINAL RELEVANCE IN SUMMARY OF INDONESIAN NEWS TEXT IN ONLINE NEWS PORTALS. Jurnal Pilar Nusa Mandiri, 18(2), 187-192. https://doi.org/10.33480/pilar.v18i2.3190
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Vol 18 No 2 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Period for September 2022
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