Optimizing Positive Content Generation in Prompt-based Abstractive Summarization with Large Language Models

Optimizing Positive Content Generation in Prompt-based Abstractive Summarization with Large Language Models | 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 Optimizing Positive Content Generation in Prompt-based Abstractive Summarization with Large Language Models Victor Monafal, Lawrence Patterson, Augustus Petrov, Nicholas Robertson This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5213130/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 The increasing demand for automated summarization systems that can generate content with not only factual accuracy but also emotional alignment has led to significant interest in developing models that can control both tone and content coherence. Introducing a novel approach to fine-tuning text generation models, this work focuses on the use of prompt engineering to steer output toward positive sentiment while preserving the essential elements of the source text. By combining sentiment-aware prompts with an iterative fine-tuning process, the model successfully balances the requirements of factual fidelity and emotional tone, producing summaries that are not only accurate but also aligned with user-specified emotional guidelines. Quantitative evaluations using ROUGE and BLEU scores, alongside sentiment analysis and diversity metrics, confirm that sentiment control enhances the linguistic richness and positivity of the generated summaries. Furthermore, the experiments reveal the trade-offs involved in maintaining content fidelity while steering tone, highlighting the importance of prompt design in managing these competing priorities. Artificial Intelligence and Machine Learning Summarization Sentiment Fine-tuning Prompt Engineering Evaluation Metrics 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-5213130","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":362764944,"identity":"3e60107e-4bbb-47d4-8fd6-6a7444551744","order_by":0,"name":"Victor Monafal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYLCCCgYLOX75wwdI0HKGQcJYcgZbAmlaEjfc4DEgTrVue+/BDwdqgLbM7vn24GfbncQG9sNHN+DTYnbmXLLEgWMScvwyZ7cb9px5ltjAk5Z2A6+WGzkG0h/YgLY05G6T4Kk4nNsgwWNGSIvxjwP/gH45kPNM8o8BcVrMJA62gbyfwyZNnC1nzphZHOwDOqznmLmxzJnD9W0E/XK8x/jGgW82cvzszc8evm07bMzPfvgYXi3IgA2JJEnLKBgFo2AUjAJ0AADPV1KAT3U8uwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0003-9914-998X","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Victor","middleName":"","lastName":"Monafal","suffix":""},{"id":362764945,"identity":"88eae530-c3fb-49cf-9d25-28b22b88683f","order_by":1,"name":"Lawrence Patterson","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lawrence","middleName":"","lastName":"Patterson","suffix":""},{"id":362764946,"identity":"7859c4d7-7eed-42eb-86f5-023577f4db23","order_by":2,"name":"Augustus Petrov","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Augustus","middleName":"","lastName":"Petrov","suffix":""},{"id":362764947,"identity":"969f4b1f-9a36-4aa4-975c-fe2a02bd1c9d","order_by":3,"name":"Nicholas Robertson","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Nicholas","middleName":"","lastName":"Robertson","suffix":""}],"badges":[],"createdAt":"2024-10-06 13:46:13","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-5213130/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5213130/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66232341,"identity":"d7579d3a-e4aa-490d-ae33-f1faa41e5bc8","added_by":"auto","created_at":"2024-10-09 04:25:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":212409,"visible":true,"origin":"","legend":"","description":"","filename":"positivecontentgeneration.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5213130/v1_covered_f22fec11-147f-4714-86eb-605749db5abb.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eOptimizing Positive Content Generation in Prompt-based Abstractive Summarization with Large Language Models\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"Summarization, Sentiment, Fine-tuning, Prompt Engineering, Evaluation Metrics","lastPublishedDoi":"10.21203/rs.3.rs-5213130/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5213130/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe increasing demand for automated summarization systems that can generate content with not only factual accuracy but also emotional alignment has led to significant interest in developing models that can control both tone and content coherence. 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