{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T21:15:41Z","timestamp":1771190141218,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643681504","type":"print"},{"value":"9781643681511","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"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":[[2020,12,1]]},"abstract":"<jats:p>Argument mining, a subfield of natural language processing and text mining, is a process of extracting argumentative text portions and identifying the role the selected texts play. Legal argument mining targets the argumentative parts of a legal text. In order to better understand how to apply legal argument mining as a step toward improving case summarization, we have assembled a sizeable set of cases and human-expert-prepared summaries annotated in terms of legal argument triples that capture the most important skeletal argument structures in a case. We report the results of applying multiple machine learning techniques to demonstrate and analyze the advantages and disadvantages of different methods to identify sentence components of these legal argument triples.<\/jats:p>","DOI":"10.3233\/faia200862","type":"book-chapter","created":{"date-parts":[[2020,12,3]],"date-time":"2020-12-03T11:53:11Z","timestamp":1606996391000},"source":"Crossref","is-referenced-by-count":14,"title":["Using Argument Mining for Legal Text Summarization"],"prefix":"10.3233","author":[{"given":"Huihui","family":"Xu","sequence":"first","affiliation":[{"name":"Intelligent Systems Program, University of Pittsburgh"}]},{"given":"Jarom\u00edr","family":"\u0160avelka","sequence":"additional","affiliation":[{"name":"School of Computer Science, Carnegie Mellon University"}]},{"given":"Kevin D.","family":"Ashley","sequence":"additional","affiliation":[{"name":"Intelligent Systems Program, University of Pittsburgh"},{"name":"Learning Research and Development Center, University of Pittsburgh"},{"name":"School of Law, University of Pittsburgh"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Legal Knowledge and Information Systems"],"original-title":[],"link":[{"URL":"http:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA200862","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,3]],"date-time":"2020-12-03T11:53:12Z","timestamp":1606996392000},"score":1,"resource":{"primary":{"URL":"http:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA200862"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,1]]},"ISBN":["9781643681504","9781643681511"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia200862","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,1]]}}}