HyperMatch: Long-form Text Matching via Hypergraph Convolutional Networks

HyperMatch: Long-form Text Matching via Hypergraph Convolutional Networks | 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 HyperMatch: Long-form Text Matching via Hypergraph Convolutional Networks Junwen Duan, Mingyi Jia, Jianbo Liao, Jianxin Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3941755/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 Semantic text matching plays a vital role in diverse domains, such as information retrieval, question answering, and recommendation. However, longer texts present challenges, including noise, long-range dependency, and cross-sentence inference. Graph-based approaches have shown effectiveness in addressing these challenges, but traditional graph structures struggle to model complex higher-order relationships in long-form texts. To overcome this limitation, we propose \textbf{HyperMatch}, a hypergraph-based method for long text matching. HyperMatch leverages hypergraph modeling to capture high-order relationships and enhance matching performance. Our approach involves constructing a keyword graph using document keywords as nodes, connecting sentences to nodes based on inclusion relationships, creating a hypergraph based on sentence similarity across nodes, and utilizing hypergraph convolutional networks to aggregate matching signals. Extensive experiments on benchmark datasets demonstrate the superiority of our model over state-of-the-art long text matching approaches. Long-form Text Matching Graph Neural Networks Hypergraph Learning Full Text Additional Declarations No competing interests reported. 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-3941755","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272505977,"identity":"b50339b0-2539-4223-b844-4f4f0d75522f","order_by":0,"name":"Junwen Duan","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Junwen","middleName":"","lastName":"Duan","suffix":""},{"id":272505978,"identity":"fe71158b-78bf-420d-8e6e-b93c535012b3","order_by":1,"name":"Mingyi Jia","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Mingyi","middleName":"","lastName":"Jia","suffix":""},{"id":272505981,"identity":"42f1534a-fc09-4ee0-8ec9-f66b0fcb9b21","order_by":2,"name":"Jianbo Liao","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jianbo","middleName":"","lastName":"Liao","suffix":""},{"id":272505982,"identity":"a6d5f318-82c8-45e1-a1e0-9a801c2a820b","order_by":3,"name":"Jianxin Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBACxgYwZcPAwMzcABVLIEpLGlALI5FaoOAwQjdBLcwzcgw/F/w6H83fDtTy489hBn72HAOGnzvwOKznjLH0zL7buTMOMzYw9rYdZpDseWPA2HsGj5b23g3SvD23cxuAWoDeOcxgcCPHgJmxDY+WZt7Nv3l7zuXOB2lhADrMnqCW9t5t0jw/DuRuAGthA9oiQUhLz/lv1rwNybkbgVoO9ral80iceVYAZODWYjgjLfk2zx+73HnnDx988OOPtRx/e/LGBz/xaWkAWQVVcACIeWAMnEAeTP7Bp2QUjIJRMApGPAAAnRFUdT5EdbUAAAAASUVORK5CYII=","orcid":"","institution":"Central South University","correspondingAuthor":true,"prefix":"","firstName":"Jianxin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-02-09 03:09:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3941755/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3941755/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51285143,"identity":"0b05714e-8075-49da-a6b0-3a8282127bd9","added_by":"auto","created_at":"2024-02-18 12:54:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":526631,"visible":true,"origin":"","legend":"","description":"","filename":"wwwSpringerhypermatch.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3941755/v1_covered_bbefe9c1-c0cf-4d71-8bec-ba393e2500f1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"HyperMatch: Long-form Text Matching via Hypergraph Convolutional Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Long-form Text Matching, Graph Neural Networks, Hypergraph Learning","lastPublishedDoi":"10.21203/rs.3.rs-3941755/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3941755/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Semantic text matching plays a vital role in diverse domains, such as information retrieval, question answering, and recommendation. 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