Performance of Machine Learning Algorithms on Automatic Summarization of Indonesian Language Texts
Automatic text summarization (ATS) has become an essential task for processing huge amounts of information efficiently. ATS has been extensively studied in resource-rich languages like English, but research on summarization for under-resourced languages, such as Bahasa Indonesia, is still limited. I...
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Department of Informatics, UIN Sunan Gunung Djati Bandung
2025-05-01
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| author | Galih Wiratmoko Husni Thamrin Endang Wahyu Pamungkas |
| author_facet | Galih Wiratmoko Husni Thamrin Endang Wahyu Pamungkas |
| author_sort | Galih Wiratmoko |
| collection | DOAJ |
| description | Automatic text summarization (ATS) has become an essential task for processing huge amounts of information efficiently. ATS has been extensively studied in resource-rich languages like English, but research on summarization for under-resourced languages, such as Bahasa Indonesia, is still limited. Indonesian presents unique linguistic challenges, including its agglutinative structure, borrowed vocabulary, and limited availability of high-quality training data. This study conducts a comparative evaluation of extractive, abstractive, and hybrid models for Indonesian text summarization, utilizing the IndoSum dataset which contains 20,000 text-summary pairs. We tested several models including LSA (Latent Semantic Analysis), LexRank, T5, and BART, to assess their effectiveness in generating summaries. The results show that the LexRank+BERT hybrid model outperforms traditional extractive methods, achieving better ROUGE precision, recall, and F-measure scores. Among the abstractive methods, the T5-Large model demonstrated the best performance, producing more coherent and semantically rich summaries compared to other models. These findings suggest that hybrid and abstractive approaches are better suited for Indonesian text summarization, especially when leveraging large-scale pre-trained language models. |
| format | Article |
| id | doaj-art-db3aaab93ec54dd7abc2db6192876b57 |
| institution | Kabale University |
| issn | 2528-1682 2527-9165 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Department of Informatics, UIN Sunan Gunung Djati Bandung |
| record_format | Article |
| series | JOIN: Jurnal Online Informatika |
| spelling | doaj-art-db3aaab93ec54dd7abc2db6192876b572025-08-20T03:46:58ZengDepartment of Informatics, UIN Sunan Gunung Djati BandungJOIN: Jurnal Online Informatika2528-16822527-91652025-05-0110119620410.15575/join.v10i1.15061511Performance of Machine Learning Algorithms on Automatic Summarization of Indonesian Language TextsGalih Wiratmoko0Husni Thamrin1https://orcid.org/0000-0001-5865-9113Endang Wahyu Pamungkas2Department of Informatics, Universitas Muhammadiyah MadiunDepartment of Informatic, Universitas Muhammadiyah SurakartaDepartment of Informatic, Universitas Muhammadiyah SurakartaAutomatic text summarization (ATS) has become an essential task for processing huge amounts of information efficiently. ATS has been extensively studied in resource-rich languages like English, but research on summarization for under-resourced languages, such as Bahasa Indonesia, is still limited. Indonesian presents unique linguistic challenges, including its agglutinative structure, borrowed vocabulary, and limited availability of high-quality training data. This study conducts a comparative evaluation of extractive, abstractive, and hybrid models for Indonesian text summarization, utilizing the IndoSum dataset which contains 20,000 text-summary pairs. We tested several models including LSA (Latent Semantic Analysis), LexRank, T5, and BART, to assess their effectiveness in generating summaries. The results show that the LexRank+BERT hybrid model outperforms traditional extractive methods, achieving better ROUGE precision, recall, and F-measure scores. Among the abstractive methods, the T5-Large model demonstrated the best performance, producing more coherent and semantically rich summaries compared to other models. These findings suggest that hybrid and abstractive approaches are better suited for Indonesian text summarization, especially when leveraging large-scale pre-trained language models.https://join.if.uinsgd.ac.id/index.php/join/article/view/1506abstractive algorithmsbahasa indonesiahybrid modelt5-modeltext summarization |
| spellingShingle | Galih Wiratmoko Husni Thamrin Endang Wahyu Pamungkas Performance of Machine Learning Algorithms on Automatic Summarization of Indonesian Language Texts JOIN: Jurnal Online Informatika abstractive algorithms bahasa indonesia hybrid model t5-model text summarization |
| title | Performance of Machine Learning Algorithms on Automatic Summarization of Indonesian Language Texts |
| title_full | Performance of Machine Learning Algorithms on Automatic Summarization of Indonesian Language Texts |
| title_fullStr | Performance of Machine Learning Algorithms on Automatic Summarization of Indonesian Language Texts |
| title_full_unstemmed | Performance of Machine Learning Algorithms on Automatic Summarization of Indonesian Language Texts |
| title_short | Performance of Machine Learning Algorithms on Automatic Summarization of Indonesian Language Texts |
| title_sort | performance of machine learning algorithms on automatic summarization of indonesian language texts |
| topic | abstractive algorithms bahasa indonesia hybrid model t5-model text summarization |
| url | https://join.if.uinsgd.ac.id/index.php/join/article/view/1506 |
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