Corpus for Automatic Structuring of Legal Documents
In populous countries, pending legal cases have been growing exponentially. There is a need for developing techniques for processing and organizing legal documents. In this paper, we introduce a new corpus for structuring legal documents. In particular, we introduce a corpus of legal judgment docume...
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creator | Kalamkar, Prathamesh Tiwari, Aman Agarwal, Astha Karn, Saurabh Gupta, Smita Raghavan, Vivek Modi, Ashutosh |
description | In populous countries, pending legal cases have been growing exponentially.
There is a need for developing techniques for processing and organizing legal
documents. In this paper, we introduce a new corpus for structuring legal
documents. In particular, we introduce a corpus of legal judgment documents in
English that are segmented into topical and coherent parts. Each of these parts
is annotated with a label coming from a list of pre-defined Rhetorical Roles.
We develop baseline models for automatically predicting rhetorical roles in a
legal document based on the annotated corpus. Further, we show the application
of rhetorical roles to improve performance on the tasks of summarization and
legal judgment prediction. We release the corpus and baseline model code along
with the paper. |
doi_str_mv | 10.48550/arxiv.2201.13125 |
format | Article |
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There is a need for developing techniques for processing and organizing legal
documents. In this paper, we introduce a new corpus for structuring legal
documents. In particular, we introduce a corpus of legal judgment documents in
English that are segmented into topical and coherent parts. Each of these parts
is annotated with a label coming from a list of pre-defined Rhetorical Roles.
We develop baseline models for automatically predicting rhetorical roles in a
legal document based on the annotated corpus. Further, we show the application
of rhetorical roles to improve performance on the tasks of summarization and
legal judgment prediction. We release the corpus and baseline model code along
with the paper.</description><identifier>DOI: 10.48550/arxiv.2201.13125</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Computation and Language ; Computer Science - Learning</subject><creationdate>2022-01</creationdate><rights>http://creativecommons.org/licenses/by-nc-nd/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,781,886</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2201.13125$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2201.13125$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Kalamkar, Prathamesh</creatorcontrib><creatorcontrib>Tiwari, Aman</creatorcontrib><creatorcontrib>Agarwal, Astha</creatorcontrib><creatorcontrib>Karn, Saurabh</creatorcontrib><creatorcontrib>Gupta, Smita</creatorcontrib><creatorcontrib>Raghavan, Vivek</creatorcontrib><creatorcontrib>Modi, Ashutosh</creatorcontrib><title>Corpus for Automatic Structuring of Legal Documents</title><description>In populous countries, pending legal cases have been growing exponentially.
There is a need for developing techniques for processing and organizing legal
documents. In this paper, we introduce a new corpus for structuring legal
documents. In particular, we introduce a corpus of legal judgment documents in
English that are segmented into topical and coherent parts. Each of these parts
is annotated with a label coming from a list of pre-defined Rhetorical Roles.
We develop baseline models for automatically predicting rhetorical roles in a
legal document based on the annotated corpus. Further, we show the application
of rhetorical roles to improve performance on the tasks of summarization and
legal judgment prediction. We release the corpus and baseline model code along
with the paper.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Computation and Language</subject><subject>Computer Science - Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotzrtuwjAUgGEvDBX0ATrhF0jqe3JGFOhFitSh7NGJLygSwcixK_r2VWmnf_v1EfLEWa1ardkzptv0VQvBeM0lF_qByC6ma1loiInuSo4z5snSz5yKzSVNlxONgfb-hGe6j7bM_pKXDVkFPC_-8b9rcnw5HLu3qv94fe92fYWm0RUIjwKg5cG4kbNgrdKNU-icR2YkOBVaZA04PwpAMGb0nDGp_BgaCwrkmmz_tnf1cE3TjOl7-NUPd738AT1CP4A</recordid><startdate>20220131</startdate><enddate>20220131</enddate><creator>Kalamkar, Prathamesh</creator><creator>Tiwari, Aman</creator><creator>Agarwal, Astha</creator><creator>Karn, Saurabh</creator><creator>Gupta, Smita</creator><creator>Raghavan, Vivek</creator><creator>Modi, Ashutosh</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20220131</creationdate><title>Corpus for Automatic Structuring of Legal Documents</title><author>Kalamkar, Prathamesh ; Tiwari, Aman ; Agarwal, Astha ; Karn, Saurabh ; Gupta, Smita ; Raghavan, Vivek ; Modi, Ashutosh</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a675-92ea29981f6db10fcc457d4addea0639d4f8a079deb29a966be10034ebf7c9493</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Computation and Language</topic><topic>Computer Science - Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Kalamkar, Prathamesh</creatorcontrib><creatorcontrib>Tiwari, Aman</creatorcontrib><creatorcontrib>Agarwal, Astha</creatorcontrib><creatorcontrib>Karn, Saurabh</creatorcontrib><creatorcontrib>Gupta, Smita</creatorcontrib><creatorcontrib>Raghavan, Vivek</creatorcontrib><creatorcontrib>Modi, Ashutosh</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kalamkar, Prathamesh</au><au>Tiwari, Aman</au><au>Agarwal, Astha</au><au>Karn, Saurabh</au><au>Gupta, Smita</au><au>Raghavan, Vivek</au><au>Modi, Ashutosh</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Corpus for Automatic Structuring of Legal Documents</atitle><date>2022-01-31</date><risdate>2022</risdate><abstract>In populous countries, pending legal cases have been growing exponentially.
There is a need for developing techniques for processing and organizing legal
documents. In this paper, we introduce a new corpus for structuring legal
documents. In particular, we introduce a corpus of legal judgment documents in
English that are segmented into topical and coherent parts. Each of these parts
is annotated with a label coming from a list of pre-defined Rhetorical Roles.
We develop baseline models for automatically predicting rhetorical roles in a
legal document based on the annotated corpus. Further, we show the application
of rhetorical roles to improve performance on the tasks of summarization and
legal judgment prediction. We release the corpus and baseline model code along
with the paper.</abstract><doi>10.48550/arxiv.2201.13125</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Artificial Intelligence Computer Science - Computation and Language Computer Science - Learning |
title | Corpus for Automatic Structuring of Legal Documents |
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