{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:47:19Z","timestamp":1778604439012,"version":"3.51.4"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T00:00:00Z","timestamp":1724889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Automatic text summarization is a cornerstone of natural language processing, yet existing methods often struggle to maintain contextual integrity and capture nuanced sentence relationships. Introducing the Optimized Auto Encoded Long Short-Term Memory Network (OAELSTM), enhanced by the Whale Optimization Algorithm (WOA), offers a novel approach to this challenge. Existing summarization models frequently produce summaries that are either too generic or disjointed, failing to preserve the essential content. The OAELSTM model, integrating deep LSTM layers and autoencoder mechanisms, focuses on extracting key phrases and concepts, ensuring that summaries are both informative and coherent. WOA fine-tunes the model\u2019s parameters, enhancing its precision and efficiency. Evaluation on datasets like CNN\/Daily Mail and Gigaword demonstrates the model\u2019s superiority over existing approaches. It achieves a ROUGE Score of 0.456, an accuracy rate of 84.47%, and a specificity score of 0.3244, all within an efficient processing time of 4,341.95\u2009s.<\/jats:p>","DOI":"10.3389\/frai.2024.1399168","type":"journal-article","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T15:11:26Z","timestamp":1724944286000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Whale-optimized LSTM networks for enhanced automatic text summarization"],"prefix":"10.3389","volume":"7","author":[{"given":"Bharathi Mohan","family":"Gurusamy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prasanna Kumar","family":"Rangarajan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Altalbe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2024,8,29]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"2528","DOI":"10.1007\/s11227-021-03950-x","article-title":"Improving the readability and saliency of abstractive text summarization using combination of deep neural networks equipped with auxiliary attention mechanism","volume":"78","author":"Aliakbarpour","year":"2021","journal-title":"J. Supercomput."},{"key":"ref2","first-page":"697","article-title":"Entity commonsense representation for neural abstractive summarization","author":"Amplayo","year":"2018"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-019-0557-y","article-title":"Character level embedding with deep convolutional neural network for text normalization of unstructured data for twitter sentiment analysis","volume":"9","author":"Arora","year":"2019","journal-title":"Soc. Netw. Anal. Min."},{"key":"ref4","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-33808-3_14","article-title":"Text summarization for big data analytics: a comprehensive review of GPT 2 and BERT approaches","volume-title":"Data analytics for internet of things infrastructure. Internet of things","author":"Bharathi Mohan","year":"2023"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"23557","DOI":"10.1109\/ACCESS.2023.3249783","article-title":"Neural attention model for abstractive text summarization using linguistic feature space","volume":"11","author":"Dilawari","year":"2023","journal-title":"IEEE Access"},{"key":"ref6","first-page":"1","article-title":"Unsupervised abstractive text summarization with length controlled autoencoder","author":"Dugar","year":"2022"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"6663","DOI":"10.11591\/ijece.v13i6.pp6663-6672","article-title":"A hybrid approach for text summarization using semantic latent Dirichlet allocation and sentence concept mapping with transformer","volume":"13","author":"Gurusamy","year":"2023","journal-title":"Int. J. Electric. Comput. Eng."},{"key":"ref8","first-page":"1","article-title":"A novel approach using extractive and abstractive summarization for the genre classification of short text","author":"Jain","year":"2023"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"105349","DOI":"10.1016\/j.compbiomed.2022.105349","article-title":"AltWOA: altruistic whale optimization algorithm for feature selection on microarray datasets","volume":"144","author":"Kundu","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref10","first-page":"1","article-title":"An automated new approach in fast text classification (fastText) a case study for Turkish text classification without pre-processing","author":"Kuyumcu","year":"2019"},{"key":"ref11","first-page":"63","article-title":"Visual audio summarization based on NLP models","author":"Latha","year":"2022"},{"key":"ref12","first-page":"274","article-title":"Next sentence prediction: the impact of preprocessing techniques in deep learning","author":"Latief","year":"2023"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/s00500-022-07617-4","article-title":"Multi-layered network model for text summarization using feature representation","volume":"27","author":"Malarselvi","year":"2023","journal-title":"Soft. Comput."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1007\/s41870-022-01080-y","article-title":"Lattice abstraction-based content summarization using baseline abstractive lexical chaining progress","volume":"15","author":"Mohan","year":"2023","journal-title":"Int. J. Inf. Tecnol."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"105858","DOI":"10.1016\/j.compbiomed.2022.105858","article-title":"Enhanced whale optimization algorithm for medical feature selection: a COVID-19 case study","volume":"148","author":"Nadimi-Shahraki","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref16","first-page":"280","article-title":"Abstractive text summarization using sequence-to-sequence RNNs and beyond","author":"Nallapati","year":"2016"},{"key":"ref17","first-page":"873","article-title":"A context-aware BERT retrieval framework utilizing abstractive summarization","author":"Pan","year":"2022"},{"key":"ref18","first-page":"1","article-title":"A novel approach for multi-document summarization using Jaccard and cosine similarity","author":"Pawar","year":"2022"},{"key":"ref19","first-page":"1","article-title":"A novel method for text summarization and clustering of documents","author":"Ramachandran","year":"2022"},{"key":"ref20","first-page":"1","article-title":"Text summarization using transformer model","author":"Ranganathan","year":"2022"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"9037","DOI":"10.1002\/int.22979","article-title":"Multiobjective whale optimization algorithm-based feature selection for intelligent systems","volume":"37","author":"Riyahi","year":"2022","journal-title":"Int. J. Intell. Syst."},{"key":"ref22","first-page":"1073","article-title":"Get to the point: summarization with pointer-generator networks","author":"See","year":"2017"},{"key":"ref23","first-page":"1","article-title":"Hate speech classification implementing NLP and CNN with machine learning algorithm through interpretable explainable AI","author":"Shakil","year":"2022"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"1934","DOI":"10.1109\/ACCESS.2022.3233854","article-title":"Token-level fact correction in abstractive summarization","volume":"11","author":"Shin","year":"2023","journal-title":"IEEE Access"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1007\/s11042-018-5749-3","article-title":"Abstractive text summarization using CNN-LSTM based deep learning","volume":"78","author":"Song","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref26","first-page":"8894","article-title":"Joint parsing and generation for abstractive summarization","author":"Song","year":"2020"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1007\/s13042-022-01653-0","article-title":"TSFNFS: two-stage-fuzzy-neighborhood feature selection with binary whale optimization algorithm","volume":"14","author":"Sun","year":"2023","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"16229","DOI":"10.1007\/s00521-021-06224-y","article-title":"Spatial bound whale optimization algorithm: an efficient high-dimensional feature selection approach","volume":"33","author":"Too","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref29","first-page":"3076","article-title":"Concept pointer network for abstractive summarization","author":"Wang","year":"2019"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"133981","DOI":"10.1109\/ACCESS.2022.3231016","article-title":"Feature based automatic text summarization methods: a comprehensive state-of-the-art survey","volume":"10","author":"Yadav","year":"2022","journal-title":"IEEE Access"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"185506","DOI":"10.1109\/ACCESS.2019.2960538","article-title":"Language model-driven topic clustering and summarization for news articles","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref32","first-page":"363","article-title":"LenAtten: an effective length controlling unit for text summarization","volume-title":"ACL\/IJCNLP (Findings)","author":"Yu","year":"2021"},{"key":"ref33","first-page":"789","article-title":"Pretraining-based natural language generation for text summarization","author":"Zhang","year":"2019"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2024.1399168\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T15:11:31Z","timestamp":1724944291000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2024.1399168\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,29]]},"references-count":33,"alternative-id":["10.3389\/frai.2024.1399168"],"URL":"https:\/\/doi.org\/10.3389\/frai.2024.1399168","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,29]]},"article-number":"1399168"}}