{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T01:15:40Z","timestamp":1768353340857,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T00:00:00Z","timestamp":1624233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2040490"],"award-info":[{"award-number":["2040490"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,6,21]]},"DOI":"10.1145\/3462757.3466098","type":"proceedings-article","created":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T06:50:46Z","timestamp":1627455046000},"page":"250-254","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Toward summarizing case decisions via extracting argument issues, reasons, and conclusions"],"prefix":"10.1145","author":[{"given":"Huihui","family":"Xu","sequence":"first","affiliation":[{"name":"University of Pittsburgh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaromir","family":"Savelka","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin D.","family":"Ashley","sequence":"additional","affiliation":[{"name":"University of Pittsburgh"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,7,27]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"A. Bansal Z. Bu B. Mishra S. Wang K. Ashley and M. Grabmair. 2016. Document Ranking with Citation Information and Oversampling Sentence Classification in the LUIMA Framework.  A. Bansal Z. Bu B. Mishra S. Wang K. Ashley and M. Grabmair. 2016. Document Ranking with Citation Information and Oversampling Sentence Classification in the LUIMA Framework."},{"key":"e_1_3_2_1_2_1","volume-title":"A coefficient of agreement for nominal scales. Educational and psychological measurement 20, 1","author":"Cohen J.","year":"1960","unstructured":"J. Cohen . 1960. A coefficient of agreement for nominal scales. Educational and psychological measurement 20, 1 ( 1960 ), 37--46. J. Cohen. 1960. A coefficient of agreement for nominal scales. Educational and psychological measurement 20, 1 (1960), 37--46."},{"key":"e_1_3_2_1_3_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2018 . Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018). Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_3_2_1_4_1","unstructured":"M. Falakmasir and K. Ashley. 2017. Utilizing Vector Space Models for Identifying Legal Factors from Text.. In JURIX. 183--192.  M. Falakmasir and K. Ashley. 2017. Utilizing Vector Space Models for Identifying Legal Factors from Text.. In JURIX. 183--192."},{"key":"e_1_3_2_1_5_1","unstructured":"A. Farzindar and G. Lapalme. 2004. Legal text summarization by exploration of the thematic structure and argumentative roles. In Text Summarization Branches Out. 27--34.  A. Farzindar and G. Lapalme. 2004. Legal text summarization by exploration of the thematic structure and argumentative roles. In Text Summarization Branches Out. 27--34."},{"key":"e_1_3_2_1_6_1","volume-title":"Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies. 987--996","author":"Feng V.","unstructured":"V. Feng and G. Hirst . 2011. Classifying arguments by scheme . In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies. 987--996 . V. Feng and G. Hirst. 2011. Classifying arguments by scheme. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies. 987--996."},{"key":"e_1_3_2_1_7_1","volume-title":"Proceedings of the HLT-NAACL 03 Text Summarization Workshop. 33--40","author":"Grover C.","unstructured":"C. Grover , B. Hachey , and C. Korycinski . 2003. Summarising legal texts: Sentential tense and argumentative roles . In Proceedings of the HLT-NAACL 03 Text Summarization Workshop. 33--40 . C. Grover, B. Hachey, and C. Korycinski. 2003. Summarising legal texts: Sentential tense and argumentative roles. In Proceedings of the HLT-NAACL 03 Text Summarization Workshop. 33--40."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/IALP48816.2019.9037668"},{"key":"e_1_3_2_1_9_1","volume-title":"Convolutional Neural Networks for Sentence Classification. CoRR abs\/1408.5882","author":"Kim Yoon","year":"2014","unstructured":"Yoon Kim . 2014. Convolutional Neural Networks for Sentence Classification. CoRR abs\/1408.5882 ( 2014 ). arXiv:1408.5882 http:\/\/arxiv.org\/abs\/1408.5882 Yoon Kim. 2014. Convolutional Neural Networks for Sentence Classification. CoRR abs\/1408.5882 (2014). arXiv:1408.5882 http:\/\/arxiv.org\/abs\/1408.5882"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.3390\/info10040150"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"J. Landis and G. Koch. 1977. The measurement of observer agreement for categorical data. Biometrics (1977) 159--174.  J. Landis and G. Koch. 1977. The measurement of observer agreement for categorical data. Biometrics (1977) 159--174.","DOI":"10.2307\/2529310"},{"key":"e_1_3_2_1_12_1","volume-title":"Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu , Myle Ott , Naman Goyal , Jingfei Du , Mandar Joshi , Danqi Chen , Omer Levy , Mike Lewis , Luke Zettlemoyer , and Veselin Stoyanov . 2019 . Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019). Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)."},{"key":"e_1_3_2_1_13_1","volume-title":"Proc. 20th ACM int'l conf. Info. and knowledge management. 383--392","author":"Lu Qi.","unstructured":"Qi. Lu , J. Conrad , K. Al-Kofahi , and W. Keenan . 2011. Legal document clustering with built-in topic segmentation . In Proc. 20th ACM int'l conf. Info. and knowledge management. 383--392 . Qi. Lu, J. Conrad, K. Al-Kofahi, and W. Keenan. 2011. Legal document clustering with built-in topic segmentation. In Proc. 20th ACM int'l conf. Info. and knowledge management. 383--392."},{"key":"e_1_3_2_1_14_1","unstructured":"S. Minaee N. Kalchbrenner E. Cambria N. Nikzad M. Chenaghlu and J. Gao. 2020. Deep learning based text classification: A comprehensive review. arXiv preprint arXiv:2004.03705 (2020).  S. Minaee N. Kalchbrenner E. Cambria N. Nikzad M. Chenaghlu and J. Gao. 2020. Deep learning based text classification: A comprehensive review. arXiv preprint arXiv:2004.03705 (2020)."},{"key":"e_1_3_2_1_15_1","volume-title":"Measuring the similarity of sentential arguments in dialog. arXiv preprint arXiv:1709.01887","author":"Misra Amita","year":"2017","unstructured":"Amita Misra , Brian Ecker , and Marilyn A Walker . 2017. Measuring the similarity of sentential arguments in dialog. arXiv preprint arXiv:1709.01887 ( 2017 ). Amita Misra, Brian Ecker, and Marilyn A Walker. 2017. Measuring the similarity of sentential arguments in dialog. arXiv preprint arXiv:1709.01887 (2017)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10506-010-9104-x"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1410"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10506-010-9087-7"},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings, 31st Int. Conf. on Legal Knowledge and Information Systems, Jurix. 111--120","author":"Savelka J.","unstructured":"J. Savelka and K. Ashley . 2018. Segmenting U.S. Court Decisions into Functional and Issue Specific Parts . In Proceedings, 31st Int. Conf. on Legal Knowledge and Information Systems, Jurix. 111--120 . J. Savelka and K. Ashley. 2018. Segmenting U.S. Court Decisions into Functional and Issue Specific Parts. In Proceedings, 31st Int. Conf. on Legal Knowledge and Information Systems, Jurix. 111--120."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10506-017-9197-6"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"A. Wyner R. Mochales-Palau M. Moens and D. Milward. 2010. Approaches to text mining arguments from legal cases. In Semantic processing of legal texts. Springer 60--79.  A. Wyner R. Mochales-Palau M. Moens and D. Milward. 2010. Approaches to text mining arguments from legal cases. In Semantic processing of legal texts. Springer 60--79.","DOI":"10.1007\/978-3-642-12837-0_4"},{"key":"e_1_3_2_1_23_1","volume-title":"Using Argument Mining for Legal Text Summarization. Legal Knowledge and Information Systems JURIX","author":"Xu Huihui","year":"2020","unstructured":"Huihui Xu , Jarom\u00edr \u0160avelka , and Kevin D Ashley . 2020. Using Argument Mining for Legal Text Summarization. Legal Knowledge and Information Systems JURIX ( 2020 ), 184--193. Huihui Xu, Jarom\u00edr \u0160avelka, and Kevin D Ashley. 2020. Using Argument Mining for Legal Text Summarization. Legal Knowledge and Information Systems JURIX (2020), 184--193."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10506-019-09242-3"}],"event":{"name":"ICAIL '21: Eighteenth International Conference for Artificial Intelligence and Law","location":"S\u00e3o Paulo Brazil","acronym":"ICAIL '21","sponsor":["SIGAI ACM Special Interest Group on Artificial Intelligence"]},"container-title":["Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3462757.3466098","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3462757.3466098","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3462757.3466098","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:28:31Z","timestamp":1750195711000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3462757.3466098"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,21]]},"references-count":24,"alternative-id":["10.1145\/3462757.3466098","10.1145\/3462757"],"URL":"https:\/\/doi.org\/10.1145\/3462757.3466098","relation":{},"subject":[],"published":{"date-parts":[[2021,6,21]]},"assertion":[{"value":"2021-07-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}