{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T19:44:30Z","timestamp":1771703070915,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684727","type":"print"},{"value":"9781643684734","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T00:00:00Z","timestamp":1701907200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,12,7]]},"abstract":"<jats:p>Complex legal language, filled with jargon, nuanced language semantics, and a high level of domain specificity, poses a significant challenge for automation in handling various legal tasks. In the realm of legal document composition, a pivotal component revolves around accurately referencing case laws and other sources to substantiate assertions and arguments. Understanding the legal domain and identifying appropriate citation context or cite-worthy sentences automatically is challenging. Our research is centered on the issue of citation-worthiness identification of a given sentence. This serves as the initial phase in contemporary citation recommendation systems, aimed at alleviating the effort involved in extracting a suitable array of citation contexts. To address this, we first introduce a labeled dataset comprising 178 million sentences, specifically tailored for detecting citation-worthy content within the legal domain. This dataset is curated from the Caselaw Access Project (CAP) (https:\/\/case.law\/). We proceeded to assess the performance of a range of deep learning models on this novel dataset. Among the models examined, the domain-specific pre-trained model consistently demonstrated superior performance, achieving an 88% F1-score in the task of detecting citation-worthy material. To enhance our insights, we employed inputXGradient explainable AI techniques to dissect the predictions, thereby identifying the tokens that contribute to specific citation classes.<\/jats:p>","DOI":"10.3233\/faia230971","type":"book-chapter","created":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T09:37:31Z","timestamp":1702287451000},"source":"Crossref","is-referenced-by-count":2,"title":["CiteCaseLAW: Citation Worthiness Detection in Caselaw for Legal Assistive Writing"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5132-9223","authenticated-orcid":false,"given":"Mann","family":"Khatri","sequence":"first","affiliation":[{"name":"IIIT Delhi"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3567-9757","authenticated-orcid":false,"given":"Reshma","family":"Sheik","sequence":"additional","affiliation":[{"name":"NIT, Trichy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7676-8108","authenticated-orcid":false,"given":"Pritish","family":"Wadhwa","sequence":"additional","affiliation":[{"name":"IIIT Delhi"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1818-3597","authenticated-orcid":false,"given":"Gitansh","family":"Satija","sequence":"additional","affiliation":[{"name":"IIIT Delhi"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7880-8219","authenticated-orcid":false,"given":"Yaman","family":"Kumar","sequence":"additional","affiliation":[{"name":"Adobe MDSR"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1028-9373","authenticated-orcid":false,"given":"Rajiv Ratn","family":"Shah","sequence":"additional","affiliation":[{"name":"IIIT Delhi"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5082-2078","authenticated-orcid":false,"given":"Ponnurangam","family":"Kumaraguru","sequence":"additional","affiliation":[{"name":"IIIT Hyderabad"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Legal Knowledge and Information Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230971","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T09:37:33Z","timestamp":1702287453000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230971"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,7]]},"ISBN":["9781643684727","9781643684734"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230971","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,7]]}}}