Abstractive Summarization of Indonesian Islamic Stories Using Long Short-Term Memory (LSTM)

The length of narratives in stories often poses a challenge for many readers, especially those with time constraints or difficulty understanding the entire story. In this case, summarization offers a solution, but manual summarization is not always efficient in meeting the need for quick and concise information. This study aims to develop an automatic text summarization system for Islamic stories using the Long Short Term Memory (LSTM) algorithm. The study employs three data splitting scenarios for training and testing: 90:10, 80:20, and 70:30. Testing results show that the highest training accuracy was achieved in the 80:20 scenario with a value of 89.44%. This does not entirely indicate that a smaller proportion of training data will always result in higher accuracy, as this improvement can be influenced by data variation, overfitting conditions, and early stopping performance. Therefore, the data division ratio influences the training process. Although the highest training accuracy was obtained in the 80:20 scenario, the best semantic summary quality was found in the 90:10 scenario. In the 90:10 scenario, the ROUGE-1 evaluation score achieved a precision of 0.4147, a recall of 0.2516, and an F1-score of 0.3027. Meanwhile, ROUGE-2 achieved a precision of 0.1022, a recall of 0.0568, and an F1-score of 0.0684. Meanwhile, ROUGE-L achieved a precision of of 0.2017, recall of 0.1209, and F1-score of 0.1459.