Enhancing newspaper article keyword generation tools in institutional repositories using AI: efficiency and accuracy
The significant advancements of Artificial Intelligence (AI) have made a substantial impact on institutional repository management. This study examines the deployment of AI technologies, specifically natural language processing (NLP) and machine learning algorithms, to enhance keyword generation for...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/113821/ http://psasir.upm.edu.my/id/eprint/113821/1/113821.pdf |
| _version_ | 1848866331547402240 |
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| author | Mohamed Fauzi, Mohamad Jefri Sayuti, Rusniah Zakaria, Azian Edawati Ibrahim, Nuraida Md Ishak, Mohamad Syahrul Nizam |
| author_facet | Mohamed Fauzi, Mohamad Jefri Sayuti, Rusniah Zakaria, Azian Edawati Ibrahim, Nuraida Md Ishak, Mohamad Syahrul Nizam |
| author_sort | Mohamed Fauzi, Mohamad Jefri |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The significant advancements of Artificial Intelligence (AI) have made a substantial impact on institutional repository management. This study examines the deployment of AI technologies, specifically natural language processing (NLP) and machine learning algorithms, to enhance keyword generation for newspaper articles. By automating the identification of relevant keywords, AI improves the discoverability, organization, and retrieval of resources within institutional repositories. The study presents a comparative analysis of AI-generated keywords versus manually curated ones, showcasing improvements in efficiency, accuracy, and relevance. Key findings indicate that AI-driven keyword generation facilitates better indexing and search capabilities, leading to increased visibility. The integration of AI in this context not only streamlines repository management but also significantly benefits researchers, librarians, and institutional stakeholders by ensuring a more efficient and user-friendly repository system. This study aims to highlight the transformative potential of AI in keyword generation, proposing a scalable and innovative approach to enhancing institutional repository functionalities. |
| first_indexed | 2025-11-15T14:18:54Z |
| format | Conference or Workshop Item |
| id | upm-113821 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:18:54Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1138212025-06-26T03:28:55Z http://psasir.upm.edu.my/id/eprint/113821/ Enhancing newspaper article keyword generation tools in institutional repositories using AI: efficiency and accuracy Mohamed Fauzi, Mohamad Jefri Sayuti, Rusniah Zakaria, Azian Edawati Ibrahim, Nuraida Md Ishak, Mohamad Syahrul Nizam The significant advancements of Artificial Intelligence (AI) have made a substantial impact on institutional repository management. This study examines the deployment of AI technologies, specifically natural language processing (NLP) and machine learning algorithms, to enhance keyword generation for newspaper articles. By automating the identification of relevant keywords, AI improves the discoverability, organization, and retrieval of resources within institutional repositories. The study presents a comparative analysis of AI-generated keywords versus manually curated ones, showcasing improvements in efficiency, accuracy, and relevance. Key findings indicate that AI-driven keyword generation facilitates better indexing and search capabilities, leading to increased visibility. The integration of AI in this context not only streamlines repository management but also significantly benefits researchers, librarians, and institutional stakeholders by ensuring a more efficient and user-friendly repository system. This study aims to highlight the transformative potential of AI in keyword generation, proposing a scalable and innovative approach to enhancing institutional repository functionalities. 2024 Conference or Workshop Item PeerReviewed image en http://psasir.upm.edu.my/id/eprint/113821/1/113821.pdf Mohamed Fauzi, Mohamad Jefri and Sayuti, Rusniah and Zakaria, Azian Edawati and Ibrahim, Nuraida and Md Ishak, Mohamad Syahrul Nizam (2024) Enhancing newspaper article keyword generation tools in institutional repositories using AI: efficiency and accuracy. In: Persidangan Tahunan Perpustakaan Malaysia 2024, 6-8 Aug. 2024, Albukhary International University. . |
| spellingShingle | Mohamed Fauzi, Mohamad Jefri Sayuti, Rusniah Zakaria, Azian Edawati Ibrahim, Nuraida Md Ishak, Mohamad Syahrul Nizam Enhancing newspaper article keyword generation tools in institutional repositories using AI: efficiency and accuracy |
| title | Enhancing newspaper article keyword generation tools in institutional repositories using AI: efficiency and accuracy |
| title_full | Enhancing newspaper article keyword generation tools in institutional repositories using AI: efficiency and accuracy |
| title_fullStr | Enhancing newspaper article keyword generation tools in institutional repositories using AI: efficiency and accuracy |
| title_full_unstemmed | Enhancing newspaper article keyword generation tools in institutional repositories using AI: efficiency and accuracy |
| title_short | Enhancing newspaper article keyword generation tools in institutional repositories using AI: efficiency and accuracy |
| title_sort | enhancing newspaper article keyword generation tools in institutional repositories using ai: efficiency and accuracy |
| url | http://psasir.upm.edu.my/id/eprint/113821/ http://psasir.upm.edu.my/id/eprint/113821/1/113821.pdf |