Peringkas Teks Otomatis Bahasa Indonesia Secara Abstraktif Menggunakan Metode Long Short-term Memory
DOI:
https://doi.org/10.34818/eoe.v8i2.14732Abstract
bstrak Salah satu topik dalam bidang Natural Language Processing (NLP) yang cukup menantang adalah peringkas teks otomatis. Dalam praktiknya peringkas teks otomatis terbagi menjadi dua pendekatan, yaitu ekstraktif dan abstraktif. Pendekatan abstraktif dinilai lebih baik karena cara kerjanya mendekati cara kerja manusia ketika meringkas teks atau yang disebut parafrase. Metode yang digunakan pada penelitian ini adalah Long Short-Term Memory (LSTM) yang mana metode tersebut telah sukses melakukan peringkasan dalam Bahasa Inggris. Dataset yang digunakan adalah kumpulan artikel berita media daring Bahasa Indonesia. Hasil terbaik yang didapatkan pada pengujian dengan metode LSTM menggunakan metode evaluasi ROUGE-1 adalah 0.13846. Kata kunci: peringkas teks otomatis, abstraktif, Bahasa Indonesia, long short-term memory, ROUGE Abstract One topic about natural language processing that is quite challenging is automatic text summarization. Automatic-text-summarization is practically divided into two kinds of approach, namely extractive and abstractive. Abstractive-approach is considered better since it resembles how humans work in terms of text summarizing or paraphrasing. A method used in this study is Long Short-Term Memory (LSTM) which has succeeded to summarize texts in English. Datasets that have been used are a number of online news articles in Bahasa Indonesia. The best result gained using LSTM based on the ROUGE-1 evaluation is 0.13846. Keywords: automatic text summarization, Bahasa Indonesia, long short-term memory, ROUGEDownloads
References
[1] S. Gupta, S. K. Gupta. 2019. Abstractive Summarization: An overview of the state of the art. ExpertSystems with Applications. 121:49 – 65.
[2] C. Khatri, G. Singh, N. Parikh. 2018. Abstractive and Extractive Text Summarization using Document Context Vector and Recurrent Neural Networks. arXiv:1807.08000.
[3] R. Adelia, Suyanto, U.N. Wisesty. 2019. Indonesian Abstractive Text Summarization Using Bidirectional Gated Recurrent Unit. Procedia Computer Science, 157, pp.581-588.
[4] A. Najibullah. 2015. Indonesian Text Summarization based on Naïve Bayes Method. Proceeding of the International Seminar and Conference 2015: The Golden Triangle (Indonesia-India-Tiongkok) Interrelations in Religion, Science, Culture, and Economic, Semarang, Indonesia, p. 12.
[5] Mustaqhfiri, M., Abidin, Z., Kusumawati, R.,2012, Peringkasan Teks Otomatis Berita Berbahasa Indonesia Menggunakan Metode Maximum Marginal Relevance. DOI: 10.18860/mat.v0i0.1578
[6] M. Fachrurrozi, N. Yusliani, R. U. Yoanita. 2013. Frequent Term based Text Summarization for Bahasa Indonesia. Proceedings of the International Conference on Innovations in Engineering and Technology, Bangkok, Thailand, p. 3.
[7] K. Kurniawan, S. Louvan. 2018. Indosum: A New Benchmark Dataset for Indonesian Text Summarization. International Conference on Asian Language Processing (IALP).
[8] K. Cho, dkk. 2014. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. arXiv:1406.1078.
[9] K. Ivanedra, M. Mustikasari. 2018. Implementasi Metode Recurrent Neural Network pada Text Summarization dengan Teknik Abstraktif. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK). 6(4) : 377-382.
[10] R. Nallapati, B. Xiang, B. Zhou. 2016. Sequence-To-Sequence Rnns For Text Summarization. Proceedings of The 20th SIGNLL. DOI: 10.18653/v1/K16-1028
[11] C. Y. Lin. 2004. Rouge: A package for automatic evaluation of summaries. Text Summarization Branches out: Proceedings of the ACL-04 Workshop, vol. 8. Barcelona, Spain.
[12] D. T. Massandy, M. L. Khodra. 2014. Guided Summarization for Indonesian News Articles. in 2014 International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA), Aug. 2014, pp. 140–145.
