Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Multi-task fine-tuning for generating keyphrases in a scientific domain. / Glazkova, Anna; Morozov, Dmitry.
Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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TY - GEN
T1 - Multi-task fine-tuning for generating keyphrases in a scientific domain
AU - Glazkova, Anna
AU - Morozov, Dmitry
N1 - This work was supported by the grant of the President of the Russian Federation no. MK-3118.2022.4. Публикация для корректировки.
PY - 2023
Y1 - 2023
N2 - Automatic selection of keyphrases (keywords) is a major challenge to finding and systematizing scholarly documents. This paper investigates the efficiency of using titles of scientific papers as additional information for keyphrase generation. We propose an approach to multi-task fine-tuning the BART model using control codes1. It is shown that the suggested approach can improve the performance of BART for the task of keyphrase generation. In some cases, the presented model outperforms state-of-the-art models for keyphrase extraction. Moreover, the results have demonstrated that multitask fine-tuning also increases the performance of title generation.
AB - Automatic selection of keyphrases (keywords) is a major challenge to finding and systematizing scholarly documents. This paper investigates the efficiency of using titles of scientific papers as additional information for keyphrase generation. We propose an approach to multi-task fine-tuning the BART model using control codes1. It is shown that the suggested approach can improve the performance of BART for the task of keyphrase generation. In some cases, the presented model outperforms state-of-the-art models for keyphrase extraction. Moreover, the results have demonstrated that multitask fine-tuning also increases the performance of title generation.
KW - BART
KW - automatic summarization
KW - keyphrase generation
KW - keyword extraction
KW - multi-task learning
KW - natural language processing
KW - scientific text
KW - text generation
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85163414123&origin=inward&txGid=2bb414d77865dfe2c854f4a3e11743ae
UR - https://www.mendeley.com/catalogue/6033603b-5b39-3ca4-9928-bc7f25a5bec5/
U2 - 10.1109/ITNT57377.2023.10139061
DO - 10.1109/ITNT57377.2023.10139061
M3 - Conference contribution
SN - 9798350397338
T3 - Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023
BT - Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
ER -
ID: 58623887