Abstractive Text Summarisation using T5 Transformer Architecture with analysis

Abstractive Text Summarisation using T5 Transformer Architecture with analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Abstractive Text Summarisation using T5 Transformer Architecture with analysis Vasanth Kumar Bhukya, Umesh Bhukya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4986903/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Now a days, Text summarization has become important as the amount of text data available online grows at an exponential rate. Most of the text classification systems require going through a huge amount of data. In general, Producing exact and meaningful summaries of big texts is a time-consuming endeavour. Hence generating abstract summaries which retain the key information of the data and using it to train machine learning models will make these models space and time-efficient. Abstractive text summarization has been successful in moving from linear models to nonlinear neural network models using sparse models [1]. This success comes from the application of deep learning models on natural language processing tasks where these mod-els are capable of modeling the interrelating patterns in data without hand-crafted features. The Text to Text Transfer Transformer(T5) approach was used to investigate the text summarization problem, and the results showed that the Transfer Learning based model performed significantly better for abstractive text summarization than the Sequence to Sequence Recurrent Model. Artificial Intelligence and Machine Learning Transformer Natural Language Processing Self Attention Transformer Encoder DecoderText-to-Text Transformer Embedding Self Attention Procedure Feed Forward Encoder-Decoder Linear and Softmax Layer Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4986903","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":345969688,"identity":"189eeade-b194-4a87-ae1a-271271532a76","order_by":0,"name":"Vasanth Kumar Bhukya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABIElEQVRIiWNgGAWjYBACAwkGZjiHmeEAmAKREjLEaGFshmhhSwBp4SFFC48BmMSlxVy6x9iYp+awvHz72eOPC84cjuaffebzqxs1FjwM7IePbsCixXLOGeNknmOHDTecyUtsnnHjcO6Mc7nbrHOOAR3Gk5Z2A5vDbuQYH+ZhS2PcwJBj2Mzz4XBuwxnebcY5bEAtEjxmuLX8S7Of3/8GomX+GZ5nxjn/8GtJ5m2zSWy4AbIF6LANZ3iYH+e24dZiOSOt2HBun03yhhtvDGfznEnP3XiGzYw5t0+Chw2HX8wlkjdLvPkmYTu/P8fgM88x69x5Z5gff875VifHz374GDYtIMCEHgVsEmASh3IQYPyBJsD8AY/qUTAKRsEoGHkAALwOaSaim3S5AAAAAElFTkSuQmCC","orcid":"","institution":"National Institute Of Technology Calicut","correspondingAuthor":true,"prefix":"","firstName":"Vasanth","middleName":"Kumar","lastName":"Bhukya","suffix":""},{"id":345969689,"identity":"8a5916df-ff59-4795-9ad3-321a523ce181","order_by":1,"name":"Umesh Bhukya","email":"","orcid":"","institution":"Computer Science and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Umesh","middleName":"","lastName":"Bhukya","suffix":""}],"badges":[],"createdAt":"2024-08-27 21:40:28","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4986903/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4986903/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63507670,"identity":"4c7e0cdf-bfdb-4362-8432-2adc5beb2068","added_by":"auto","created_at":"2024-08-29 02:24:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":630963,"visible":true,"origin":"","legend":"","description":"","filename":"textsummarisationfinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4986903/v1_covered_cd6dee61-7c72-46ea-89a0-ba3e43a6042f.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAbstractive Text Summarisation using T5 Transformer Architecture with analysis\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"National Institute of Technology Calicut","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Transformer, Natural Language Processing, Self Attention, Transformer, Encoder, DecoderText-to-Text Transformer, Embedding, Self Attention Procedure, Feed Forward Encoder-Decoder, Linear and Softmax Layer","lastPublishedDoi":"10.21203/rs.3.rs-4986903/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4986903/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNow a days, Text summarization has become important as the amount of text data available online grows at an exponential rate. 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