Peringkas Otomatis Teks Berbahasa Arab Menggunakan Algoritma TextRank

  • Muhammad Fikri Hidayattullah Politeknik Harapan Bersama
  • Ardhiyan Azizi Universitas Ahmad Dahlan


Increasingly, the amount of data in the form of text documents scattered on the internet is getting bigger. It took a very long time to get the information from each of these documents. For this reason, several researchers developed the Automatic Text Summarizer to summarize text automatically, so that the time needed to get important information from the entire document can be faster. Research that focuses on automatic summarization of Arabic texts is very rare. In fact, there are more than 300 million Arabic speakers in the world and Arabic is the official language at the United Nations. Therefore, this study develops a model that can perform text summarization automatically using the TextRank algorithm. The test results using Q&A Evaluation show very good results with details of the suitability of the summary results with the original text by 90%, the suitability of the summary results with Arabic grammar is 91.43%, the suitability of the summary results is 90%, the ease of understanding the summary results is 90%. and the useful aspects of the model developed were 91.43%.


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S. Subramanian, R. Li, J. Pilault, and C. Pal, “On Extractive and Abstractive Neural Document Summarization with Transformer Language Models”, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9308-9319, Nov. 2020.

Silvia, P. Rukmana, and V. R. Aprilia, “Aplikasi Automatic Text Summarizer,” Comput. Sci. Dep. Bina Nusant., vol. 1969, p. 7, 2010.

I. S. Pratama, G. Alam, and Tinaliah, “Penerapan Algoritma Centroid-Based Summarization untuk Sistem Peringkasan Dokumen Berbahasa Indonesia,” no. x, 2015.

L. Al Qassem, D. Wang, H. Barada, A. Al-Rubaie, and N. Almoosa, “Automatic Arabic Text Summarization Based on Fuzzy Logic,” Proc. 3rd Int. Conf. Nat. Lang. Speech Process., pp. 42–48, 2019.

F. Kiyani and O. Tas, “A survey automatic text summarization,” Pressacademia, vol. 5, no. 1, pp. 205–213, 2017.

L. M. Al Qassem, D. Wang, Z. Al Mahmoud, H. Barada, A. Al-Rubaie, and N. I. Almoosa, “Automatic Arabic Summarization: A survey of methodologies and systems,” Procedia Comput. Sci., vol. 117, pp. 10–18, 2017.

A. B. Al-Saleh and M. E. B. Menai, “Automatic Arabic text summarization: a survey,” Artif. Intell. Rev., vol. 45, no. 2, pp. 203–234, 2016.

Eris, V. C. M, and J. Pragantha, “Penerapan Algoritma Textrank Untuk Automatic Summarization Pada Dokumen Berbahasa Indonesia,” J. Ilmu Tek. dan Komput., vol. 1, no. 1, pp. 71–78, 2017.

R. Jayashree, S. Murthy K., and B. S. Anami, “Categorized Text Document Summarization in the Kannada Language by sentence ranking,” Int. Conf. Intell. Syst. Des. Appl. ISDA, pp. 776–781, 2012.

N. Q. Uy, P. T. Anh, T. C. Doan, and N. X. Hoai, “A study on the use of genetic programming for automatic text summarization,” Proc. - 4th Int. Conf. Knowl. Syst. Eng. KSE 2012, pp. 93–98, 2012.

H. Saggion et al., “Automatic Text Summarization : Past , Present and Future,” 2016.

R. Mihalcea and P. Tarau, “TextRank: Bringing Order into Texts,” 2004.

R. Elbarougy, G. Behery, and A. El, “Extractive Arabic Text Summarization Using Modified PageRank Algorithm,” Egypt. Informatics J., vol. 21, no. 2, pp. 73–81, 2020.

How to Cite
Hidayattullah, M. F., & Azizi, A. (2021). Peringkas Otomatis Teks Berbahasa Arab Menggunakan Algoritma TextRank. Jurnal Ilmiah Informatika, 6(1), 33-42.
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