@inproceedings{ali-etal-2023-legal,
title = "Legal Argument Extraction from Court Judgements using Integer Linear Programming",
author = "Ali, Basit and
Pawar, Sachin and
Palshikar, Girish and
Sinha Banerjee, Anindita and
Singh, Dhirendra",
editor = "Alshomary, Milad and
Chen, Chung-Chi and
Muresan, Smaranda and
Park, Joonsuk and
Romberg, Julia",
booktitle = "Proceedings of the 10th Workshop on Argument Mining",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.argmining-1.6",
doi = "10.18653/v1/2023.argmining-1.6",
pages = "52--63",
abstract = "Legal arguments are one of the key aspects of legal knowledge which are expressed in various ways in the unstructured text of court judgements. A large database of past legal arguments can be created by extracting arguments from court judgements, categorizing them, and storing them in a structured format. Such a database would be useful for suggesting suitable arguments for any new case. In this paper, we focus on extracting arguments from Indian Supreme Court judgements using minimal supervision. We first identify a set of certain sentence-level argument markers which are useful for argument extraction such as whether a sentence contains a claim or not, whether a sentence is argumentative in nature, whether two sentences are part of the same argument, etc. We then model the legal argument extraction problem as a text segmentation problem where we combine multiple weak evidences in the form of argument markers using Integer Linear Programming (ILP), finally arriving at a global document-level solution giving the most optimal legal arguments. We demonstrate the effectiveness of our technique by comparing it against several competent baselines.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ali-etal-2023-legal">
<titleInfo>
<title>Legal Argument Extraction from Court Judgements using Integer Linear Programming</title>
</titleInfo>
<name type="personal">
<namePart type="given">Basit</namePart>
<namePart type="family">Ali</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sachin</namePart>
<namePart type="family">Pawar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Girish</namePart>
<namePart type="family">Palshikar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anindita</namePart>
<namePart type="family">Sinha Banerjee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dhirendra</namePart>
<namePart type="family">Singh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 10th Workshop on Argument Mining</title>
</titleInfo>
<name type="personal">
<namePart type="given">Milad</namePart>
<namePart type="family">Alshomary</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chung-Chi</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Smaranda</namePart>
<namePart type="family">Muresan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Joonsuk</namePart>
<namePart type="family">Park</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Julia</namePart>
<namePart type="family">Romberg</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Singapore</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Legal arguments are one of the key aspects of legal knowledge which are expressed in various ways in the unstructured text of court judgements. A large database of past legal arguments can be created by extracting arguments from court judgements, categorizing them, and storing them in a structured format. Such a database would be useful for suggesting suitable arguments for any new case. In this paper, we focus on extracting arguments from Indian Supreme Court judgements using minimal supervision. We first identify a set of certain sentence-level argument markers which are useful for argument extraction such as whether a sentence contains a claim or not, whether a sentence is argumentative in nature, whether two sentences are part of the same argument, etc. We then model the legal argument extraction problem as a text segmentation problem where we combine multiple weak evidences in the form of argument markers using Integer Linear Programming (ILP), finally arriving at a global document-level solution giving the most optimal legal arguments. We demonstrate the effectiveness of our technique by comparing it against several competent baselines.</abstract>
<identifier type="citekey">ali-etal-2023-legal</identifier>
<identifier type="doi">10.18653/v1/2023.argmining-1.6</identifier>
<location>
<url>https://aclanthology.org/2023.argmining-1.6</url>
</location>
<part>
<date>2023-12</date>
<extent unit="page">
<start>52</start>
<end>63</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Legal Argument Extraction from Court Judgements using Integer Linear Programming
%A Ali, Basit
%A Pawar, Sachin
%A Palshikar, Girish
%A Sinha Banerjee, Anindita
%A Singh, Dhirendra
%Y Alshomary, Milad
%Y Chen, Chung-Chi
%Y Muresan, Smaranda
%Y Park, Joonsuk
%Y Romberg, Julia
%S Proceedings of the 10th Workshop on Argument Mining
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ali-etal-2023-legal
%X Legal arguments are one of the key aspects of legal knowledge which are expressed in various ways in the unstructured text of court judgements. A large database of past legal arguments can be created by extracting arguments from court judgements, categorizing them, and storing them in a structured format. Such a database would be useful for suggesting suitable arguments for any new case. In this paper, we focus on extracting arguments from Indian Supreme Court judgements using minimal supervision. We first identify a set of certain sentence-level argument markers which are useful for argument extraction such as whether a sentence contains a claim or not, whether a sentence is argumentative in nature, whether two sentences are part of the same argument, etc. We then model the legal argument extraction problem as a text segmentation problem where we combine multiple weak evidences in the form of argument markers using Integer Linear Programming (ILP), finally arriving at a global document-level solution giving the most optimal legal arguments. We demonstrate the effectiveness of our technique by comparing it against several competent baselines.
%R 10.18653/v1/2023.argmining-1.6
%U https://aclanthology.org/2023.argmining-1.6
%U https://doi.org/10.18653/v1/2023.argmining-1.6
%P 52-63
Markdown (Informal)
[Legal Argument Extraction from Court Judgements using Integer Linear Programming](https://aclanthology.org/2023.argmining-1.6) (Ali et al., ArgMining-WS 2023)
ACL