Evaluating the Effectiveness of the LexRank and LSA Algorithm in Automatic Text Summarization for Indonesian Language
Main Article Content
The aim of this study is to evaluate how effective the Lexrank algorithm and Latent semantic analysis (LSA) are in automatic text summarization for the Indonesian language. This research focuses on natural language processing and handling of excessive data. We applied both algorithms to generate text summaries using the INDOSUM dataset, which contains about 20,000 news articles in Indonesian with manual summaries. To assess performance, the ROUGE metric was used, which includes aspects of precision, recall, and F1 score. In all tested metrics, LSA outperformed Lexrank. LSA had a precision of 0.57, recall of 0.67, and an F1 score of 0.59, whereas Lexrank had a precision of 0.46, recall of 0.52, and an F1 score of 0.48. These result indicate that LSA is better at gathering important information from the original text than Lexrank.
Keywords:
Automatic Text Summarization, Latent Sematic Analysis, Lexrank
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Hernández-Castañeda, Ángel, García-Hernández, René Arnulfo, Ledeneva, Yulia, & Millán-Hernández, Christian Eduardo. (2020). Extractive automatic text summarization based on lexical-semantic keywords. IEEE Access, 8, 49896–49907.
J. N. Madhuri, “Extractive Text Summarization Using Sentence Ranking,” 2019 Int. Conf. Data Sci. Commun., pp. 1–3, 2019.
Khan, Bilal, Shah, Zohaib Ali, Usman, Muhammad, Khan, Inayat, & Niazi, Badam. (2023). Exploring the landscape of automatic text summarization: a comprehensive survey. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3322188
Kurniawan, Kemal, & Louvan, Samuel. (2018). Indosum: A new benchmark dataset for indonesian text summarization. 2018 International Conference on Asian Language Processing (IALP), 215–220. https://doi.org/10.1109/IALP.2018.8629109.
Mridha, Muhammad Firoz, Lima, Aklima Akter, Nur, Kamruddin, Das, Sujoy Chandra, Hasan, Mahmud, & Kabir, Muhammad Mohsin. (2021). A survey of automatic text summarization: Progress, process and challenges. IEEE Access, 9, 156043–156070. https://doi.org/10.1109/ACCESS.2021.3129786.
Shah, Prachi, & Desai, Nikita P. (2016). A survey of automatic text summarization techniques for Indian and foreign languages. 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 4598–4601. https://doi.org/10.1109/ICEEOT.2016.7755587.
Y. Kumar, K. Kaur, and S. Kaur, Study of automatic text summarization approaches in different languages, vol. 54, no. 8. Springer Netherlands, 2021. doi: 10.1007/s10462-021-09964-4.
Wahab, Muhammad Hafizul H., Ali, Nor Hafiza, Hamid, Nor Asilah Wati A., Subramaniam, Shamala K., Latip, Rohaya, & Othman, Mohamed. (2023). A Review on Optimization-Based Automatic Text Summarization Approach. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3348075.
Widyassari, Adhika Pramita, Affandy, Affandy, Noersasongko, Edy, Fanani, Ahmad Zainul, Syukur, Abdul, & Basuki, Ruri Suko. (2019). Literature review of automatic text summarization: research trend, dataset and method. 2019 International Conference on Information and Communications Technology (ICOIACT), 491–496. https://doi.org/10.1109/ICOIACT46704.2019.8938454.
W. S. El-kassas, C. Salama, A. Rafea, and H. K. Mohamed, “Automatic Text Summarization : A Comprehensive Survey,” no. July, 2020, doi: 10.1016/j.eswa.2020.113679.
Wu, Kang, Shi, Ping, & Pan, Da. (2015). An approach to automatic summarization for chinese text based on the combination of spectral clustering and LexRank. 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 1350–1354. https://doi.org/10.1109/FSKD.2015.7382140
