ISRA, ALIF AKBAR MUHAMMAD and Yusliani, Novi and Rachmatullah, Muhammad Naufal (2024) EXTRACTIVE SUMMARIZATION BERITA BERBAHASA INDONESIA MENGGUNAKAN LSTM. Undergraduate thesis, Sriwijaya University.
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Abstract
In the internet era, the vast amount of available information makes it challenging for readers to find relevant news and requires time to read entire documents to extract desired information. Therefore, summaries are needed to help readers extract and represent the most crucial information from news articles efficiently and effectively. This research focuses on developing an Extractive Summarization model for Indonesian news texts using Long Short-Term Memory (LSTM) networks due to their ability to maintain memory of word relationships. The designed model achieved a ROUGE-1 Precision of 0.3287, Recall of 0.6045, and F1-score of 0.4207 as well as a ROUGE-2 Precision of 0.2159, Recall of 0.4351, and F1-score of 0.2841, with a Loss of 0.09 and Validation Loss of 0.0898. The dataset comprised 18,952 data points, with 14,469 for training, 2,241 for validation, and 2,242 for testing
| Item Type: | Thesis (Undergraduate) |
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| Uncontrolled Keywords: | Automatic Text Summarization, Extractive Summarization, LSTM, Natural Language Processing, Indonesian News Text, ROUGE Metrics |
| Subjects: | Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. |
| Divisions: | 09-Faculty of Computer Science > 55201-Informatics (S1) |
| Depositing User: | Alif Akbar Muhammad Isra |
| Date Deposited: | 28 Aug 2024 04:52 |
| Last Modified: | 28 Aug 2024 04:52 |
| URI: | http://repository.unsri.ac.id/id/eprint/156343 |
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