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Keyphrase extraction by the use of glove and ResNeXt optimized by enhanced human evolutionary optimization (EHEO) algorithm.

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Bibliographic Details
Title: Keyphrase extraction by the use of glove and ResNeXt optimized by enhanced human evolutionary optimization (EHEO) algorithm.
Authors: Pan C; Hunan Mass Media Vocational and Technical College, Changsha, 410100, Hunan, China., Liu Y; Hunan Mass Media Vocational and Technical College, Changsha, 410100, Hunan, China. L76ysh@163.com., Sarabi M; Sharif University of Technology, Tehran, Iran. sarabimohammad1990@gmail.com.; College of Technical Engineering, The Islamic University, Najaf, Iraq. sarabimohammad1990@gmail.com.
Source: Scientific reports [Sci Rep] 2025 Nov 24; Vol. 15 (1), pp. 41529. Date of Electronic Publication: 2025 Nov 24.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Imprint Name(s): Original Publication: London : Nature Publishing Group, copyright 2011-
MeSH Terms: Algorithms* , Natural Language Processing* , Data Mining*/methods, Humans ; Neural Networks, Computer ; Support Vector Machine
Abstract: Competing Interests: Declarations. Competing interests: The authors declare no competing interests.
Keyphrase extraction (KPE) is an essential process in natural language processing, facilitating the document content summarization for diverse uses like search engine optimization and information retrieval. Nevertheless, manual extraction can be labor-intensive, and automated techniques often face challenges in understanding contextual relationships within the text. This research introduces an innovative method that employs the ResNeXt neural network architecture, optimized by an enhanced human evolutionary optimization algorithm, and integrated with GloVe-100 word embeddings. The model was evaluated utilizing the KP20k dataset, a commonly utilized resource that includes approximately 500,000 scientific papers labeled with keyphrases, the Inspec database that comprises 2,000 English abstracts, and the SemEval-2010 benchmark dataset that comprises 244 research papers. The proposed model was evaluated against other advanced approaches, such as Convolutional Neural Network, k-Nearest Neighbors, Support Vector Machine, BERT, Convolutional Neural Network-BERT, and Gated Recurrent Unit, and outperformed them with recall, precision, and F1-score values of 98.81%, 98.67%, and 98.74%, respectively. It could achieve 96.54%, 96.32%, and 96.43% in precision, recall, and F1-score on the Inspect dataset, respectively. Moreover, the proposed model indicated remarkable performance over the SemEval-2010 dataset by achieving 97.32% precision, 97.81% recall, and an F1-score of 97.56%. These findings highlighted the efficacy of integrating high-quality embeddings and optimization strategies with advanced neural architectures. The suggested model provided a strong and effective solution for automatic extraction, which can be potentially used in various fields.
(© 2025. The Author(s).)
References: Sci Rep. 2024 Oct 28;14(1):25731. (PMID: 39468285)
Contributed Indexing: Keywords: Enhanced human evolutionary optimization algorithm; Glove-100; Keyphrase extraction; Natural language processing; ResNeXt
Entry Date(s): Date Created: 20251125 Date Completed: 20251125 Latest Revision: 20251128
Update Code: 20251128
PubMed Central ID: PMC12644682
DOI: 10.1038/s41598-025-25487-2
PMID: 41285939
Database: MEDLINE
Description
Abstract:Competing Interests: Declarations. Competing interests: The authors declare no competing interests.<br />Keyphrase extraction (KPE) is an essential process in natural language processing, facilitating the document content summarization for diverse uses like search engine optimization and information retrieval. Nevertheless, manual extraction can be labor-intensive, and automated techniques often face challenges in understanding contextual relationships within the text. This research introduces an innovative method that employs the ResNeXt neural network architecture, optimized by an enhanced human evolutionary optimization algorithm, and integrated with GloVe-100 word embeddings. The model was evaluated utilizing the KP20k dataset, a commonly utilized resource that includes approximately 500,000 scientific papers labeled with keyphrases, the Inspec database that comprises 2,000 English abstracts, and the SemEval-2010 benchmark dataset that comprises 244 research papers. The proposed model was evaluated against other advanced approaches, such as Convolutional Neural Network, k-Nearest Neighbors, Support Vector Machine, BERT, Convolutional Neural Network-BERT, and Gated Recurrent Unit, and outperformed them with recall, precision, and F1-score values of 98.81%, 98.67%, and 98.74%, respectively. It could achieve 96.54%, 96.32%, and 96.43% in precision, recall, and F1-score on the Inspect dataset, respectively. Moreover, the proposed model indicated remarkable performance over the SemEval-2010 dataset by achieving 97.32% precision, 97.81% recall, and an F1-score of 97.56%. These findings highlighted the efficacy of integrating high-quality embeddings and optimization strategies with advanced neural architectures. The suggested model provided a strong and effective solution for automatic extraction, which can be potentially used in various fields.<br /> (© 2025. The Author(s).)
ISSN:2045-2322
DOI:10.1038/s41598-025-25487-2