PERINGKASAN TEKS BERITA DALAM BAHASA INDONESIA SECARA ABSTRAKTIF MENGGUNAKAN METODE ATTENTION-BASED BILSTM (BIDIRECTIONAL LONG SHORT TERM MEMORY)

GAYATRI, HANNY PUTRI and Utami, Alvi Syahrini and Rizqie, M. Qurhanul (2024) PERINGKASAN TEKS BERITA DALAM BAHASA INDONESIA SECARA ABSTRAKTIF MENGGUNAKAN METODE ATTENTION-BASED BILSTM (BIDIRECTIONAL LONG SHORT TERM MEMORY). Undergraduate thesis, Sriwijaya University.

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Abstract

News is a form of information published to the public, allowing for a better understanding of the world around them. The advancement of internet technology has significantly increased the growth of Indonesian-language news sites and created a surge in information availability. Reading the entire news article takes a lot of time and makes it difficult to receive all the information quickly and accurately, leading to the concept of automatic text summarization. The BiLSTM (Bidirectional Long Short-Term Memory) algorithm is an artificial neural network architecture commonly used in text summarization tasks, especially with the addition of an attention layer that helps focus on important parts of the text. Abstractive summarization, which works by understanding and generating new sentences from the original document, is used in this study. Evaluations show that the BiLSTM model achieved a ROUGE-1 score of 0.0594, while the Hybrid model achieved a ROUGE-1 score of 0.1316.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	News, Technology, Automatic Summarization, Bidirectional Long Short-Term Memory, Attention, Abstractive, ROUGE
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Hanny Putri Gayatri
Date Deposited:	18 Jul 2024 05:29
Last Modified:	18 Jul 2024 05:29
URI:	http://repository.unsri.ac.id/id/eprint/151488

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