{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T08:11:02Z","timestamp":1778832662946,"version":"3.51.4"},"reference-count":27,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:00:00Z","timestamp":1758672000000},"content-version":"vor","delay-in-days":266,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004410","name":"T\u00fcrkiye Bilimsel ve Teknolojik Ara\u015ft\u0131rma Kurumu","doi-asserted-by":"publisher","award":["5240007"],"award-info":[{"award-number":["5240007"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Procedia Computer Science"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1016\/j.procs.2025.09.342","type":"journal-article","created":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T22:13:13Z","timestamp":1762467193000},"page":"2215-2224","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Automated Keyword Generation for Academic Research Articles Using Large Language Models"],"prefix":"10.1016","volume":"270","author":[{"given":"Mert Arda","family":"Asar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alper","family":"Mitincik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sultan","family":"Turhan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G\u00fcnce Keziban","family":"Orman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.procs.2025.09.342_bib1","doi-asserted-by":"crossref","unstructured":"Alqaryouti, O., Khwileh, H., Farouk, T., Nabhan, A., Shaalan, K., 2018. Graph-based keyword extraction. Intelligent Natural Language Processing: Trends and Applications, 159\u2013172.","DOI":"10.1007\/978-3-319-67056-0_9"},{"key":"10.1016\/j.procs.2025.09.342_bib2","doi-asserted-by":"crossref","first-page":"7228","DOI":"10.3390\/app13127228","article-title":"Unlocking the potential of keyword extraction: the need for access to high-quality datasets","volume":"13","author":"Amur","year":"2023","journal-title":"Applied Sciences"},{"key":"10.1016\/j.procs.2025.09.342_bib3","doi-asserted-by":"crossref","unstructured":"Bezawada, B., Nayak, C., 2022. Writing abstract and keywords of scientific article-do\u2019s and don\u2019ts. Advancements in Homeopathic Research 7. doi: 10.48165\/ahr.2022.7.3.1.","DOI":"10.48165\/ahr.2022.7.3.1"},{"key":"10.1016\/j.procs.2025.09.342_bib4","unstructured":"Chataut, S., Do, T., Gurung, B.D.S., Aryal, S., Khanal, A., Lushbough, C., Gnimpieba, E., 2024. Comparative study of domain driven terms extraction using large language models. arXiv preprint arXiv:2404.02330."},{"key":"10.1016\/j.procs.2025.09.342_bib5","doi-asserted-by":"crossref","first-page":"1923","DOI":"10.1007\/s11192-020-03576-5","article-title":"Keyword-citation-keyword network: A new perspective of discipline knowledge structure analysis","volume":"124","author":"Cheng","year":"2020","journal-title":"Scientometrics"},{"key":"10.1016\/j.procs.2025.09.342_bib6","doi-asserted-by":"crossref","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K., 2019. Bert: Pre-training of deep bidirectional transformers for language understanding, in: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pp. 4171\u20134186.","DOI":"10.18653\/v1\/N19-1423"},{"key":"10.1016\/j.procs.2025.09.342_bib7","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1017\/S1351324919000457","article-title":"Keyword extraction: Issues and methods","volume":"26","author":"Firoozeh","year":"2020","journal-title":"Natural Language Engineering"},{"key":"10.1016\/j.procs.2025.09.342_bib8","doi-asserted-by":"crossref","first-page":"104104","DOI":"10.1016\/j.ipm.2025.104104","article-title":"Qfas-ke: Query focused answer summarization using keyword extraction","volume":"62","author":"Goyal","year":"2025","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.procs.2025.09.342_bib9","unstructured":"Grootendorst, M., 2020. Keybert: Minimal keyword extraction with bert. URL: https:\/\/doi.org\/10.5281\/zenodo.4461265, doi: 10.5281\/zenodo.4461265."},{"key":"10.1016\/j.procs.2025.09.342_bib10","doi-asserted-by":"crossref","first-page":"71805","DOI":"10.1109\/ACCESS.2022.3188861","article-title":"Multifeature fusion keyword extraction algorithm based on textrank","volume":"10","author":"Guo","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.procs.2025.09.342_bib11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40747-021-00343-8","article-title":"A patent keywords extraction method using textrank model with prior public knowledge","volume":"8","author":"Huang","year":"2022","journal-title":"Complex & Intelligent Systems"},{"key":"10.1016\/j.procs.2025.09.342_bib12","unstructured":"Kang, B., Shin, Y., 2025. Empirical study of zero-shot keyphrase extraction with large language models, in: Proceedings of the 31st International Conference on Computational Linguistics, pp. 3670\u20133686."},{"key":"10.1016\/j.procs.2025.09.342_bib13","first-page":"77","article-title":"An improved textrank keyword extraction method based on the watts-strogatz model","volume":"3","author":"Li","year":"2024","journal-title":"Inf. Dyn. Appl"},{"key":"10.1016\/j.procs.2025.09.342_bib14","series-title":"Llm-take: Theme-aware keyword extraction using large language models, in: 2023 IEEE International Conference on Big Data (BigData)","first-page":"4318","author":"Maragheh","year":"2023"},{"key":"10.1016\/j.procs.2025.09.342_bib15","doi-asserted-by":"crossref","unstructured":"Mihalcea, R., Tarau, P., 2004. Textrank: Bringing order into text, in: Proceedings of the 2004 conference on empirical methods in natural language processing, pp. 404\u2013411.","DOI":"10.3115\/1220575.1220627"},{"key":"10.1016\/j.procs.2025.09.342_bib16","doi-asserted-by":"crossref","unstructured":"Mu, Y., Dong, C., Bontcheva, K., Song, X., 2024. Large language models ofer an alternative to the traditional approach of topic modelling. arXiv preprint arXiv:2403.16248.","DOI":"10.63317\/2x489fw7wi5m"},{"key":"10.1016\/j.procs.2025.09.342_bib17","unstructured":"Nussbaum, Z., Morris, J.X., Duderstadt, B., Mulyar, A., 2024. Nomic embed: Training a reproducible long context text embedder. arXiv preprint arXiv:2402.01613."},{"key":"10.1016\/j.procs.2025.09.342_bib18","doi-asserted-by":"crossref","unstructured":"Patel, K., Caragea, C., 2021. Exploiting position and contextual word embeddings for keyphrase extraction from scientific papers, in: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 1585\u20131591.","DOI":"10.18653\/v1\/2021.eacl-main.136"},{"key":"10.1016\/j.procs.2025.09.342_bib19","unstructured":"Qiu, D., Zheng, Q., 2022. Improving textrank algorithm for automatic keyword extraction with tolerance rough set. International Journal of Fuzzy Systems, 1\u201311."},{"key":"10.1016\/j.procs.2025.09.342_bib20","series-title":"Using tf-idf to determine word relevance in document queries, in: Proceedings of the first instructional conference on machine learning","first-page":"29","author":"Ramos","year":"2003"},{"key":"10.1016\/j.procs.2025.09.342_bib21","series-title":"The google pagerank algorithm and how it works","author":"Rogers","year":"2002"},{"key":"10.1016\/j.procs.2025.09.342_bib22","doi-asserted-by":"crossref","unstructured":"Salinas, A., Morstatter, F., 2024. The butterfly effect of altering prompts: How small changes and jailbreaks affect large language model performance. arXiv preprint arXiv:2401.03729.","DOI":"10.18653\/v1\/2024.findings-acl.275"},{"key":"10.1016\/j.procs.2025.09.342_bib23","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/978-981-15-9651-3_23","article-title":"A review on word embedding techniques for text classification","volume":"2020","author":"Selva Birunda","year":"2021","journal-title":"Innovative Data Communication Technologies and Application: Proceedings of ICIDCA"},{"key":"10.1016\/j.procs.2025.09.342_bib24","doi-asserted-by":"crossref","first-page":"185","DOI":"10.33880\/ejfm.2022110401","article-title":"The importance of keywords and references in a scientific manuscript","volume":"11","author":"Sezer","year":"2022","journal-title":"Eurasian journal of family medicine"},{"key":"10.1016\/j.procs.2025.09.342_bib25","doi-asserted-by":"crossref","first-page":"9285324","DOI":"10.1155\/2022\/9285324","article-title":"Application of an improved tf-idf method in literary text classification","volume":"2022","author":"Xiang","year":"2022","journal-title":"Advances in Multimedia"},{"key":"10.1016\/j.procs.2025.09.342_bib26","doi-asserted-by":"crossref","first-page":"886","DOI":"10.26599\/TST.2020.9010051","article-title":"News keyword extraction algorithm based on semantic clustering and word graph model","volume":"26","author":"Xiong","year":"2021","journal-title":"Tsinghua Science and Technology"},{"key":"10.1016\/j.procs.2025.09.342_bib27","doi-asserted-by":"crossref","first-page":"10657","DOI":"10.3390\/app142210657","article-title":"Iwf-textrank keyword extraction algorithm modelling","volume":"14","author":"Zhang","year":"2024","journal-title":"Applied Sciences"}],"container-title":["Procedia Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050925030157?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050925030157?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T07:34:13Z","timestamp":1778830453000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1877050925030157"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":27,"alternative-id":["S1877050925030157"],"URL":"https:\/\/doi.org\/10.1016\/j.procs.2025.09.342","relation":{},"ISSN":["1877-0509"],"issn-type":[{"value":"1877-0509","type":"print"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Automated Keyword Generation for Academic Research Articles Using Large Language Models","name":"articletitle","label":"Article Title"},{"value":"Procedia Computer Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.procs.2025.09.342","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}]}}