{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T14:13:00Z","timestamp":1777385580354,"version":"3.51.4"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T00:00:00Z","timestamp":1738195200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T00:00:00Z","timestamp":1738195200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100009367","name":"Mansoura University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100009367","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>With the rise of Arabic digital content, effective summarization methods are essential. Current Arabic text summarization systems face challenges such as language complexity and vocabulary limitations. We introduce an innovative framework using Arabic Named Entity Recognition to enhance abstractive summarization, crucial for NLP applications like question answering and knowledge graph construction. Our model, based on natural language generation techniques, adapts to diverse datasets. It identifies key information, synthesizes it into coherent summaries, and ensures grammatical accuracy through deep learning. Evaluated on the EASC dataset, our model achieved a 74% ROUGE1 score and a 97.6% accuracy in semantic coherence, with high readability and relevance scores. This sets a new standard for Arabic text summarization, greatly improving NLP information processing.<\/jats:p>","DOI":"10.1007\/s00521-024-10949-x","type":"journal-article","created":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T13:04:03Z","timestamp":1738242243000},"page":"7279-7301","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Enhanced model for abstractive Arabic text summarization using natural language generation and named entity recognition"],"prefix":"10.1007","volume":"37","author":[{"given":"Nada","family":"Essa","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4644-1835","authenticated-orcid":false,"given":"M. M.","family":"El-Gayar","sequence":"additional","affiliation":[]},{"given":"Eman M.","family":"El-Daydamony","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,30]]},"reference":[{"key":"10949_CR1","doi-asserted-by":"crossref","first-page":"38012","DOI":"10.1109\/ACCESS.2022.3163292","volume":"10","author":"A Elsaid","year":"2022","unstructured":"Elsaid A, Mohammed A, Ibrahim LF, Sakre MM (2022) A Comprehensive Review of Arabic Text Summarization. IEEE Access 10:38012\u201338030","journal-title":"IEEE Access"},{"issue":"117","key":"10949_CR2","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.procs.2017.10.088","volume":"1","author":"LM Al Qassem","year":"2017","unstructured":"Al Qassem LM, Wang D, Al Mahmoud Z, Barada H, Al-Rubaie A, Almoosa NI (2017) Automatic arabic summarization: a survey of methodologies and systems. Proced Comput Sci 1(117):10\u20138","journal-title":"Proced Comput Sci"},{"key":"10949_CR3","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s10462-015-9442-x","volume":"45","author":"AB Al-Saleh","year":"2016","unstructured":"Al-Saleh AB, Menai MEB (2016) Automatic Arabic text summarization: a survey. Artif Intell Rev 45:203\u2013234","journal-title":"Artif Intell Rev"},{"key":"10949_CR4","doi-asserted-by":"crossref","first-page":"6077","DOI":"10.3233\/JIFS-213589","volume":"43","author":"MA Elmenshawy","year":"2022","unstructured":"Elmenshawy MA, Hamza T, El-Deeb R (2022) Automatic arabic text summarization (AATS). J Intel Fuzzy Syst 43:6077\u20136092","journal-title":"J Intel Fuzzy Syst"},{"issue":"2","key":"10949_CR5","doi-asserted-by":"publisher","first-page":"437","DOI":"10.3390\/electronics12020437","volume":"12","author":"YAAL Khassawneh","year":"2023","unstructured":"Khassawneh YAAL, Hanandeh ES (2023) Extractive Arabic text summarization-graph-based approach. Electronics 12(2):437. https:\/\/doi.org\/10.3390\/electronics12020437","journal-title":"Electronics"},{"key":"10949_CR6","doi-asserted-by":"crossref","unstructured":"Gholamrezazadeh S, Salehi MA, Gholamzadeh B. A comprehensive survey on text summarization systems. Proceedings of the 2009 2nd International Conference on Computer Science and Its Applications, CSA 2009.","DOI":"10.1109\/CSA.2009.5404226"},{"issue":"729","key":"10949_CR7","first-page":"38","volume":"286","author":"P Deshpande","year":"2022","unstructured":"Deshpande P, Jahirabadkar S (2022) A survey on statistical approaches for abstractive summarization of low resource language documents. Lecture Notes Network Syst 286(729):38","journal-title":"Lecture Notes Network Syst"},{"key":"10949_CR8","first-page":"32","volume":"9","author":"I El Bazi","year":"2025","unstructured":"El Bazi I, Laachfoubi N (2025) (2019) Arabic named entity recognition using deep learning approach. Int J Electr Comput Eng 9:32","journal-title":"Int J Electr Comput Eng"},{"key":"10949_CR9","first-page":"439","volume":"9623","author":"M Gridach","year":"2018","unstructured":"Gridach M (2018) Deep learning approach for arabic named entity recognition. Lecture Notes Comput Sci Includ Subser Lect Notes Artif Intell Lecture Note Bioinformat 9623:439\u201351","journal-title":"Lecture Notes Comput Sci Includ Subser Lect Notes Artif Intell Lecture Note Bioinformat"},{"key":"10949_CR10","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/978-3-319-39345-2_8","volume":"55","author":"A Dandashi","year":"2016","unstructured":"Dandashi A, Al Jaam J, Foufou S (2016) Arabic named entity recognition\u2014a survey and analysis. Smart Innovat Syst Technol 55:83\u201396","journal-title":"Smart Innovat Syst Technol"},{"key":"10949_CR11","unstructured":"Benajiba Y, Rosso P. ANERsys 2.0: Conquering the NER Task for the Arabic Language by Combining the Maximum Entropy with POS-tag Information. Indian International Conference on Artificial Intelligence. 2007."},{"key":"10949_CR12","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s10579-011-9165-9","volume":"46","author":"R Steinberger","year":"2012","unstructured":"Steinberger R (2012) A survey of methods to ease the development of highly multilingual text mining applications. Lang Resour Eval 46:155\u2013176","journal-title":"Lang Resour Eval"},{"key":"10949_CR13","doi-asserted-by":"crossref","first-page":"37736","DOI":"10.1109\/ACCESS.2020.2973319","volume":"8","author":"M Al-Smadi","year":"2020","unstructured":"Al-Smadi M, Al-Zboon S, Jararweh Y, Juola P (2020) Transfer learning for arabic named entity recognition with deep neural networks. IEEE Access 8:37736\u201337745","journal-title":"IEEE Access"},{"issue":"23","key":"10949_CR14","doi-asserted-by":"crossref","first-page":"11944","DOI":"10.3390\/app122311944","volume":"12","author":"H Chouikhi","year":"2022","unstructured":"Chouikhi H, Alsuhaibani M (2022) Deep transformer language models for arabic text summarization: a comparison study. Appl Sciences. 12(23):11944","journal-title":"Appl Sciences."},{"key":"10949_CR15","unstructured":"Kahla M, Nov\u00e1k A, Yang ZG (2009) Fine-tuning and multilingual pre-training for abstractive summarization task for the Arabic language. Annales Mathematicae et Informaticae. 2023;57:24\u201316. Traboulsi H. Arabic named entity extraction: A local grammar-based approach. Proceedings of the International Multiconference on Computer Science and Information Technology, IMCSIT \u201909. 2009;4:19\u201323."},{"issue":"1","key":"10949_CR16","first-page":"1566890","volume":"2022","author":"YM Wazery","year":"2022","unstructured":"Wazery YM, Saleh ME, Alharbi A, Ali AA (2022) Abstractive Arabic text summarization based on deep learning. Computat Intell Neurosci 2022(1):1566890","journal-title":"Computat Intell Neurosci"},{"key":"10949_CR17","first-page":"143","volume":"4394","author":"Y Benajiba","year":"2024","unstructured":"Benajiba Y, Rosso P, Ruiz JMB (2024) ANERsys: an arabic named entity recognition system based on maximum entropy. Lecture Note Comput Sci Includ Subser Lecture Note Artif Intell Lectur Note Bioinformat 4394:143\u201353","journal-title":"Lecture Note Comput Sci Includ Subser Lecture Note Artif Intell Lectur Note Bioinformat"},{"key":"10949_CR18","first-page":"8340","volume":"96","author":"RE Salah","year":"2018","unstructured":"Salah RE, Zakaria LQB (2018) Building the classical arabic named entity recognition corpus (Canercorpus). J Theor Appl Inf Technol 96:8340\u20138351","journal-title":"J Theor Appl Inf Technol"},{"key":"10949_CR19","doi-asserted-by":"crossref","unstructured":"Burmani N, Alami H, Lafkiar S, Zouitni M, Taleb M, Nahnahi NE. Graph based method for Arabic text summarization. 2022 International Conference on Intelligent Systems and Computer Vision, ISCV 2022. 2022","DOI":"10.1109\/ISCV54655.2022.9806127"},{"key":"10949_CR20","doi-asserted-by":"crossref","unstructured":"Reda A, Salah N, Adel J, Ehab M, Ahmed I, Magdy M, et al. A Hybrid Arabic Text Summarization Approach based on Transformers. MIUCC 2022\u20142nd International Mobile, Intelligent, and Ubiquitous Computing Conference. 2022 pp 56\u201362","DOI":"10.1109\/MIUCC55081.2022.9781694"},{"key":"10949_CR21","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107791","volume":"237","author":"D Suleiman","year":"2022","unstructured":"Suleiman D, Awajan A (2022) Multilayer encoder and single-layer decoder for abstractive Arabic text summarization. Knowl Based Syst 237:107791","journal-title":"Knowl Based Syst"},{"issue":"713","key":"10949_CR22","first-page":"26","volume":"15","author":"AT Al-Taani","year":"2022","unstructured":"Al-Taani AT, Al-Sayadi SH (2022) Extractive text summarization of arabic multi-document using fuzzy C-means and latent dirichlet allocation. Int J Syst Assurance Eng Managem 15(713):26","journal-title":"Int J Syst Assurance Eng Managem"},{"key":"10949_CR23","doi-asserted-by":"crossref","unstructured":"Monroe W, Green S (2014) Manning CD. Word Segmentation of Informal Arabic with Domain Adaptation. 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014\u2014Proceedings of the Conference, 2 206 11","DOI":"10.3115\/v1\/P14-2034"},{"key":"10949_CR24","doi-asserted-by":"crossref","unstructured":"Abdelali A, Darwish K, Durrani N, Mubarak H. Farasa: A Fast and Furious Segmenter for Arabic. NAACL-HLT 2016\u20142016 Conference of the North American chapter of the association for computational linguistics: human language technologies, proceedings of the demonstrations session. 2016; 11\u20136.","DOI":"10.18653\/v1\/N16-3003"},{"key":"10949_CR25","doi-asserted-by":"crossref","unstructured":"Solyman A, Wang Z, Tao Q. Proposed model for Arabic grammar error correction based on convolutional neural network. Proceedings of the International Conference on Computer, Control, Electrical, and Electronics Engineering 2019, ICCCEEE 2019. 2019.","DOI":"10.1109\/ICCCEEE46830.2019.9071310"},{"issue":"109","key":"10949_CR26","first-page":"2020","volume":"7","author":"M Al-Maleh","year":"2020","unstructured":"Al-Maleh M, Desouki S (2020) Arabic text summarization using deep learning approach. J Big Data 7(109):2020","journal-title":"J Big Data"},{"issue":"3","key":"10949_CR27","first-page":"26","volume":"4","author":"HS AlGhanem","year":"2020","unstructured":"AlGhanem HS, Ajamiah RH (2020) Arabic text summarization approaches: A comparison study. Int J Informat Technol Language Stud 4(3):26\u201338","journal-title":"Int J Informat Technol Language Stud"},{"key":"10949_CR28","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.procs.2017.10.088","volume":"117","author":"LM Al Qassem","year":"2017","unstructured":"Al Qassem LM, Wang D, MahmoudZ Al et al (2017) Automatic Arabic summarization: a survey of methodologies and systems. Proced Comput Sci 117:10\u201318","journal-title":"Proced Comput Sci"},{"key":"10949_CR29","doi-asserted-by":"publisher","first-page":"113421","DOI":"10.1016\/j.eswa.2020.113421","volume":"157","author":"S Hou","year":"2020","unstructured":"Hou S, Zhang S, Fei C (2020) Rhetorical structure theory: a comprehensive review of theory, parsing methods and applications. Expert Syst Appl 157:113421. https:\/\/doi.org\/10.1016\/j.eswa.2020.113421","journal-title":"Expert Syst Appl"},{"key":"10949_CR30","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1613\/jair.5477","volume":"61","author":"A Gatt","year":"2018","unstructured":"Gatt A, Krahmer E (2018) Survey of the state of the art in natural language generation: core tasks, applications and evaluation. J Artific Intelligen Res 61:65\u2013170","journal-title":"J Artific Intelligen Res"},{"key":"10949_CR31","unstructured":"Steven B. NLTK: the natural language toolkit. Proceedings of the COLING\/ACL 2006 Interactive Presentation Sessions. 2006."},{"key":"10949_CR32","unstructured":"El-Haj M, Kruschwitz U, Fox C (2010) Using mechanical turk to create a corpus of Arabic summaries. In: Language Resources (LRs) and Human Language Technologies (HLT) for Semitic Languages workshop held in conjunction with the 7th International Language Resources and Evaluation Conference (LREC 2010)"},{"key":"10949_CR33","unstructured":"Yegnanarayana B. Artificial Neural Networks in Pattern Recognition: 4th IAPR TC3 Workshop, ANNPR 2010, Cairo, Egypt, April 11\u201313, Proceedings 2010."},{"key":"10949_CR34","doi-asserted-by":"crossref","unstructured":"Solyman A, Wang Z, Tao Q (2019) \"Proposed model for Arabic grammar error correction based on convolutional neural network. International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). IEEE.","DOI":"10.1109\/ICCCEEE46830.2019.9071310"},{"issue":"5","key":"10949_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3373266","volume":"19","author":"M Alkhatib","year":"2020","unstructured":"Alkhatib M, Monem AA, Shaalan K (2020) Deep learning for Arabic error detection and correction. ACM Transact Asian Low Resour Language Informat Process 19(5):1\u201313. https:\/\/doi.org\/10.1145\/3373266","journal-title":"ACM Transact Asian Low Resour Language Informat Process"},{"issue":"4","key":"10949_CR36","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.jksuci.2019.02.005","volume":"33","author":"C Moukrim","year":"2021","unstructured":"Moukrim C, Abderrahim T, Benlahmer E, Tarik A (2021) An innovative approach to autocorrecting grammatical errors in Arabic texts. J King Saud Univer Comput Informat Sci 33(4):476\u2013488","journal-title":"J King Saud Univer Comput Informat Sci"},{"key":"10949_CR37","doi-asserted-by":"crossref","unstructured":"Zaki A, Khalil M, and Abbas H. Deep architectures for abstractive text summarization in multiple languages. 2019 14th International Conference on Computer Engineering and Systems (ICCES). IEEE, 2019.","DOI":"10.1109\/ICCES48960.2019.9068171"},{"key":"10949_CR38","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.procs.2017.10.088","volume":"117","author":"L Al Qassema","year":"2017","unstructured":"Al Qassema L, Wanga D, Al Mahmouda Z, Baradab H, Al-Rubaiea A, Almoosa N (2017) Automatic Arabic summarization: a survey of methodologies and systems. Procedia Computer Science 117:10\u201318","journal-title":"Procedia Computer Science"},{"key":"10949_CR39","unstructured":"Post M. A call for clarity in reporting BLEU scores. arXiv preprint arXiv:1804.08771.2018."},{"key":"10949_CR40","doi-asserted-by":"crossref","unstructured":"Papineni K, Roukos K, Ward T, and Zhu W. Bleu: a method for automatic evaluation of machine translation. Proceedings of the 40th annual meeting of the Association for Computational Linguistics. 311\u2013318 2002.","DOI":"10.3115\/1073083.1073135"},{"key":"10949_CR41","unstructured":"Arab GPT (n.d.) Retrieved January 10, 2025, from https:\/\/arabgpt.ae\/#arabgpt"},{"key":"10949_CR42","unstructured":"Harman D, Over P. The effects of human variation in duc summarization evaluation. Text summarization branches out. 2004."},{"key":"10949_CR43","unstructured":"Napoles C, Gormley M, and Durme B. Annotated gigaword. Proceedings of the joint workshop on automatic knowledge base construction and web-scale knowledge extraction (AKBC-WEKEX). 95\u2013100. 2012."},{"key":"10949_CR44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3366\/cult.2013.0030","volume":"2","author":"M El-Haj","year":"2013","unstructured":"El-Haj M, Koulali R (2013) KALIMAT a multipurpose Arabic Corpus. Culture 2:1\u2013359","journal-title":"Culture"},{"key":"10949_CR45","unstructured":"Saad M, Ashour W. Osac: Open source arabic corpora.6th International Conference on Electrical and Computer Systems.10. 2010."},{"key":"10949_CR46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.dib.2019.104076","volume":"25","author":"O Einea","year":"2019","unstructured":"Einea O, Elnagar A, Al DR (2019) Sanad: single-label arabic news articles dataset for automatic text categorization. Data Brief 25:1\u20135","journal-title":"Data Brief"},{"issue":"9","key":"10949_CR47","first-page":"206","volume":"9","author":"N Alalyani","year":"2018","unstructured":"Alalyani N, Marie-Sainte S (2018) NADA: new Arabic dataset for text classification.\". Int J Adv Comput Sci Appl 9(9):206\u2013212","journal-title":"Int J Adv Comput Sci Appl"},{"key":"10949_CR48","doi-asserted-by":"crossref","unstructured":"Hasan T , Bhattacharjee A ,Islam M , Samin K, Li3 Y , Kang Y , Rahman S , Shahriyar R. XL-sum: Large-scale multilingual abstractive summarization for 44 languages.\u00a0arXiv preprint arXiv:2106.13822 (2021).","DOI":"10.18653\/v1\/2021.findings-acl.413"},{"key":"10949_CR49","doi-asserted-by":"crossref","unstructured":".Ladhak F, Durmus E , Cardie C, and McKeown K: A new benchmark dataset for cross-lingual abstractive summarization. arXiv preprint arXiv:2010.03093, 2020.","DOI":"10.18653\/v1\/2020.findings-emnlp.360"},{"issue":"3","key":"10949_CR50","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1093\/ptj\/85.3.257","volume":"85","author":"J Sim","year":"2005","unstructured":"Sim J, Wright CC (2005) The kappa statistic in reliability studies: use, interpretation, and sample size requirements. Phys Ther 85(3):257\u2013268","journal-title":"Phys Ther"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10949-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10949-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10949-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T18:59:03Z","timestamp":1742497143000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10949-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,30]]},"references-count":50,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["10949"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10949-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,30]]},"assertion":[{"value":"16 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}},{"value":"There are no any ethical conflicts.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}