Email Summarizer: A Novel Hybrid Approach to Email Summarization

Email Summarizer: A Novel Hybrid Approach to Email Summarization | 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 Email Summarizer: A Novel Hybrid Approach to Email Summarization Rahul Kumar Yadav, Anupama Namburu, Siddhant Sharma, Qutaiba Humadi Mohammed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7974857/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 Email has become an essential mode of communication, but the sheer volume of messages makes it difficult for users to stay organized and quickly find key information. This paper explores the field of automatic email summarization, reviewing a range of summarization techniques and how their effectiveness is evaluated. Building on this groundwork, we present a new approach designed to create clear, informative summaries by combining three complementary strategies. First, we identify important terms using TF-IDF (Term Frequency–Inverse Document Frequency) to determine which words carry the most weight in an email. Second, we apply Latent Dirichlet Allocation (LDA) to uncover the underlying themes or topics within the message. Third, we leverage sentence embeddings from the MiniLM transformer model to capture the deeper meaning of each sentence. By integrating these methods in a unified framework, our system evaluates sentence importance in the context of the entire email and its subject. We developed this solution with efficiency in mind and tested it against benchmark methods such as LexRank Summarizer, Lead Sentence, and Random Sentence selection. Results show that our approach generates summaries that are more informative and easier to understand than these baselines. This work serves both as a review of current summarization techniques and as a practical contribution toward reducing the problem of email overload with an effective, accessible summarization system. Computer Architecture and Engineering Extractive approach Abstractive Approach Statistical approach Email summarizer Evaluation techniques Full Text Additional Declarations The authors declare no competing interests. Supplementary Files emailSummaiser.zip Latex files, that include all to generate pdf 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. 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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-7974857","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":536601113,"identity":"ab81629f-5ffa-483b-9eef-bcc28cad1368","order_by":0,"name":"Rahul Kumar 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