ANALISIS KINERJA MODEL T5 DAN BART DALAM KEYPHRASE GENERATION PADA DATASET ARTIKEL ILMIAH

DERI, HASBI HUSSEIN and Novi, Yusliani and Rachmatullah, Muhammad Naufal (2025) ANALISIS KINERJA MODEL T5 DAN BART DALAM KEYPHRASE GENERATION PADA DATASET ARTIKEL ILMIAH. Undergraduate thesis, Sriwijaya University.

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Abstract

Keyphrase Generation is a task in Natural Language Processing that aims to extract keyphrases that represent the main content of a document. The increase in the number of scientific publications has made the manual annotation process inefficient and prone to subjectivity. Therefore, Automated Keyphrase Generation (AKG) has become a relevant approach to support more efficient scientific information management. This study compares the performance of two transformer-based keyphrase generation models, namely Text-to-Text Transfer Transformer (T5) and Bidirectional and Auto-Regressive Transformer (BART). Both models were trained using a fine-tuning approach on 10,000 samples from the KP20K dataset, with 1,000 samples each used for validation and testing. Testing was also conducted on other datasets consisting of other scientific article datasets, namely Inspec, SemEval, NUS, and Krapivin. The performance of both models was evaluated using the F1-Score metric to assess the agreement between the predictions and the reference key phrases. The experimental results show that T5 consistently outperforms BART, with an F1-Score of 21.01% at F1@5 and 22.36% at F1@M.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Keyphrase generation, T5, BART, Transformer, Artikel Ilmiah
Subjects:	P Language and Literature > P Philology. Linguistics > P98-98.5 Computational linguistics. Natural language processing Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76 Computer software
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Hasbi Hussein Deri
Date Deposited:	09 Jul 2025 05:16
Last Modified:	09 Jul 2025 05:16
URI:	http://repository.unsri.ac.id/id/eprint/177335

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