Nesterov-accelerated Adaptive Moment Estimation NADAM-LSTM based text summarization1.
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| Title: | Nesterov-accelerated Adaptive Moment Estimation NADAM-LSTM based text summarization1. |
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| Authors: | Radhakrishnan, P.1 (AUTHOR) rksiva13@gmail.com, Senthilkumar, G.1 (AUTHOR) |
| Source: | Journal of Intelligent & Fuzzy Systems. 2024, Vol. 46 Issue 3, p6781-6793. 13p. |
| Subjects: | Text summarization |
| Abstract: | Automatic text summarization is the task of creating concise and fluent summaries without human intervention while preserving the meaning of the original text document. To increase the readability of the languages, a summary should be generated. In this paper, a novel Nesterov-accelerated Adaptive Moment Estimation Optimization based on Long Short-Term Memory [NADAM-LSTM] has been proposed to summarize the text. The proposed NADAM-LSTM model involves three stages namely pre-processing, summary generation, and parameter tuning. Initially, the Giga word Corpus dataset is pre-processed using Tokenization, Word Removal, Stemming, Lemmatization, and Normalization for removing irrelevant data. In the summary generation phase, the text is converted to the word-to-vector method. Further, the text is fed to LSTM to summarize the text. The parameter of the LSTM is then tuned using NADAM Optimization. The performance analysis of the proposed NADAM-LSTM is calculated based on parameters like accuracy, specificity, Recall, Precision, and F1 score. The suggested NADAM-LSTM achieves an accuracy range of 99.5%. The result illustrates that the proposed NADAM-LSTM enhances the overall accuracy better than 12%, 2.5%, and 1.5% in BERT, CNN-LSTM, and RNN respectively. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 176366268 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Nesterov-accelerated Adaptive Moment Estimation NADAM-LSTM based text summarization<superscript>1</superscript>. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Radhakrishnan%2C+P%2E%22">Radhakrishnan, P.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rksiva13@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Senthilkumar%2C+G%2E%22">Senthilkumar, G.</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Intelligent+%26+Fuzzy+Systems%22">Journal of Intelligent & Fuzzy Systems</searchLink>. 2024, Vol. 46 Issue 3, p6781-6793. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Text+summarization%22">Text summarization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Automatic text summarization is the task of creating concise and fluent summaries without human intervention while preserving the meaning of the original text document. To increase the readability of the languages, a summary should be generated. In this paper, a novel Nesterov-accelerated Adaptive Moment Estimation Optimization based on Long Short-Term Memory [NADAM-LSTM] has been proposed to summarize the text. The proposed NADAM-LSTM model involves three stages namely pre-processing, summary generation, and parameter tuning. Initially, the Giga word Corpus dataset is pre-processed using Tokenization, Word Removal, Stemming, Lemmatization, and Normalization for removing irrelevant data. In the summary generation phase, the text is converted to the word-to-vector method. Further, the text is fed to LSTM to summarize the text. The parameter of the LSTM is then tuned using NADAM Optimization. The performance analysis of the proposed NADAM-LSTM is calculated based on parameters like accuracy, specificity, Recall, Precision, and F1 score. The suggested NADAM-LSTM achieves an accuracy range of 99.5%. The result illustrates that the proposed NADAM-LSTM enhances the overall accuracy better than 12%, 2.5%, and 1.5% in BERT, CNN-LSTM, and RNN respectively. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3233/JIFS-224299 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 6781 Subjects: – SubjectFull: Text summarization Type: general Titles: – TitleFull: Nesterov-accelerated Adaptive Moment Estimation NADAM-LSTM based text summarization1. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Radhakrishnan, P. – PersonEntity: Name: NameFull: Senthilkumar, G. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: 2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 10641246 Numbering: – Type: volume Value: 46 – Type: issue Value: 3 Titles: – TitleFull: Journal of Intelligent & Fuzzy Systems Type: main |
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