Legal Document Summarization: Enhancing Judicial Efficiency through Automation Detection

Legal Document Summarization: Enhancing Judicial Efficiency through Automation Detection | 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 Legal Document Summarization: Enhancing Judicial Efficiency through Automation Detection Jeffrey Wang, Ruilin Nong, Jianan Liu, Lucas Evans This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7210104/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 Legal document summarization represents a significant advancement towards improving judicial efficiency through the automation of key information detection. Our approach leverages state-of-the-art natural language processing techniques to meticulously identify and extract essential data from extensive legal texts, which facilitates a more efficient review process. By employing advanced machine learning algorithms, the framework recognizes underlying patterns within judicial documents to create precise summaries that encapsulate the crucial elements. This automation alleviates the burden on legal professionals, concurrently reducing the likelihood of overlooking vital information that could lead to errors. Through comprehensive experiments conducted with actual legal datasets, we demonstrate the capability of our method to generate high-quality summaries while preserving the integrity of the original content and enhancing processing times considerably. The results reveal marked improvements in operational efficiency, allowing legal practitioners to direct their efforts toward critical analytical and decision-making activities instead of manual reviews. This research highlights promising technology-driven strategies that can significantly alter workflow dynamics within the legal sector, emphasizing the role of automation in refining judicial processes. Computer Architecture and Engineering Legal Document Summarization Automation Detection 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. 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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-7210104","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490614906,"identity":"e11b44e3-0f30-43ec-bb44-d8bc97af6f68","order_by":0,"name":"Jeffrey Wang","email":"","orcid":"","institution":"University of Utah","correspondingAuthor":false,"prefix":"","firstName":"Jeffrey","middleName":"","lastName":"Wang","suffix":""},{"id":490614907,"identity":"d61217fd-276c-4ce3-a59b-652524a99447","order_by":1,"name":"Ruilin Nong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIie3RMWrDMBSAYQmBvAjPLzjYV7AwlBQKuUAOIVHolKFT11oVKIu7O7dob+As8eJ01mjTCwQ8tEOhtTN1kj0Wqn8U+tATDyGf7w+WEKza8ydl61pXCMajaoLwndZ8X4Qxao4yn0VQU5uI0ThDdsNzNIfgUhpg7Eqqkn201wbFoRW4v3eQAKRewepO6uj5NV8YlC2sIFHpfkXZlB2lWZ4uRL5YQQlzTWaHTwv6LQvYdiN5nCbN4QkqSjOALR6JSKcI3ynNVUHjYThewhvwfdPpyEUSEnTvX8Mq00C3PTzcJGF9e+idg/2OwGX/OJ8Lhrvn+Xd9Pp/vH/UD08FQ6wITmjIAAAAASUVORK5CYII=","orcid":"","institution":"Tianjin University","correspondingAuthor":true,"prefix":"","firstName":"Ruilin","middleName":"","lastName":"Nong","suffix":""},{"id":490614908,"identity":"abdac350-a85e-496d-bb2a-5f44b084cb5d","order_by":2,"name":"Jianan Liu","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Jianan","middleName":"","lastName":"Liu","suffix":""},{"id":490614909,"identity":"03c431d6-4a07-431a-bf95-29e7d9e9957e","order_by":3,"name":"Lucas Evans","email":"","orcid":"","institution":"Pennsylvania State University","correspondingAuthor":false,"prefix":"","firstName":"Lucas","middleName":"","lastName":"Evans","suffix":""}],"badges":[],"createdAt":"2025-07-25 04:02:03","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-7210104/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7210104/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87684692,"identity":"50a42b5e-4c1d-4e0f-ba13-8d1f2e1a5ca9","added_by":"auto","created_at":"2025-07-28 01:54:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":868132,"visible":true,"origin":"","legend":"","description":"","filename":"LegalDocumentSummarizationEnhancingJudicialEfficiencythroughAutomationDetection.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7210104/v1_covered_56085547-614c-4840-b3ff-7228c416a630.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eLegal Document Summarization: Enhancing Judicial Efficiency through Automation Detection\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Tianjin University","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":"Legal Document Summarization, Automation Detection","lastPublishedDoi":"10.21203/rs.3.rs-7210104/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7210104/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLegal document summarization represents a significant advancement towards improving judicial efficiency through the automation of key information detection. 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