Advanced extractive summarization of Indonesian texts using LSTM models

Faisal, Muhammad ORCID: https://orcid.org/0000-0003-4884-7254, Halmahera, Shafira, Cahyani, Vivin Octavia, Aziz, Abd, Afrah, Ashri Shabrina and Supriyono, Supriyono ORCID: https://orcid.org/0000-0002-4733-9189 (2024) Advanced extractive summarization of Indonesian texts using LSTM models. Presented at 2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 29-30 August 2024, Yogyakarta, Indonesia.

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Abstract

In the current digital era, the volume of available information continues to increase, especially in the form of online articles. This surge presents a challenge for readers to quickly grasp the core information. This study aims to develop an advanced extractive summarization model for Indonesian texts using Long Short-Term Memory (LSTM) networks. The research process involves several key steps: preprocessing the text data, feature extraction using Word2Vec, training the LSTM model, and evaluating the generated summaries using ROUGE-1 metrics. The LSTM model is optimized with a learning rate of 0.001 and trained over 30–50 epochs. The results demonstrate that the LSTM model achieves superior performance in generating coherent and informative summaries. The highest accuracy attained by the model is 0.88 with the optimal performance observed at a learning rate of 0.001 and 50 epochs. Evaluation based on ROUGE-1 metrics indicates that the model can produce summaries with a high F-measure of 0.875, highlighting its effectiveness in automatic text summarization. This research underscores the significant potential of LSTM-based models in enhancing extractive text summarization for Indonesian articles. The findings contribute to the field by demonstrating the practical application of advanced machine learning techniques to address the challenge of information overload.

Item Type:	Conference (Paper)
Keywords:	Extractive Text Summarization; Long Short-Term Memory (LSTM); Indonesian Language; Natural Language Processing (NLP)
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080107 Natural Language Processing
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Muhammad Faisal
Date Deposited:	10 Feb 2025 10:00

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