<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="joshi-etal-2022-graph">
    <titleInfo>
        <title>Graph-based Keyword Planning for Legal Clause Generation from Topics</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Sagar</namePart>
        <namePart type="family">Joshi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sumanth</namePart>
        <namePart type="family">Balaji</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Aparna</namePart>
        <namePart type="family">Garimella</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Vasudeva</namePart>
        <namePart type="family">Varma</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Natural Legal Language Processing Workshop 2022</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Nikolaos</namePart>
            <namePart type="family">Aletras</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ilias</namePart>
            <namePart type="family">Chalkidis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Leslie</namePart>
            <namePart type="family">Barrett</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Cătălina</namePart>
            <namePart type="family">Goan\textcommabelowtă</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Daniel</namePart>
            <namePart type="family">Preo\textcommabelowtiuc-Pietro</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Abu Dhabi, United Arab Emirates (Hybrid)</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Generating domain-specific content such as legal clauses based on minimal user-provided information can be of significant benefit in automating legal contract generation. In this paper, we propose a controllable graph-based mechanism that can generate legal clauses using only the topic or type of the legal clauses. Our pipeline consists of two stages involving a graph-based planner followed by a clause generator. The planner outlines the content of a legal clause as a sequence of keywords in the order of generic to more specific clause information based on the input topic using a controllable graph-based mechanism. The generation stage takes in a given plan and generates a clause. The pipeline consists of a graph-based planner followed by text generation. We illustrate the effectiveness of our proposed two-stage approach on a broad set of clause topics in contracts.</abstract>
    <identifier type="citekey">joshi-etal-2022-graph</identifier>
    <identifier type="doi">10.18653/v1/2022.nllp-1.26</identifier>
    <location>
        <url>https://aclanthology.org/2022.nllp-1.26</url>
    </location>
    <part>
        <date>2022-12</date>
        <extent unit="page">
            <start>276</start>
            <end>286</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Graph-based Keyword Planning for Legal Clause Generation from Topics
%A Joshi, Sagar
%A Balaji, Sumanth
%A Garimella, Aparna
%A Varma, Vasudeva
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goan\textcommabelowtă, Cătălina
%Y Preo\textcommabelowtiuc-Pietro, Daniel
%S Proceedings of the Natural Legal Language Processing Workshop 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F joshi-etal-2022-graph
%X Generating domain-specific content such as legal clauses based on minimal user-provided information can be of significant benefit in automating legal contract generation. In this paper, we propose a controllable graph-based mechanism that can generate legal clauses using only the topic or type of the legal clauses. Our pipeline consists of two stages involving a graph-based planner followed by a clause generator. The planner outlines the content of a legal clause as a sequence of keywords in the order of generic to more specific clause information based on the input topic using a controllable graph-based mechanism. The generation stage takes in a given plan and generates a clause. The pipeline consists of a graph-based planner followed by text generation. We illustrate the effectiveness of our proposed two-stage approach on a broad set of clause topics in contracts.
%R 10.18653/v1/2022.nllp-1.26
%U https://aclanthology.org/2022.nllp-1.26
%U https://doi.org/10.18653/v1/2022.nllp-1.26
%P 276-286

Markdown (Informal)

[Graph-based Keyword Planning for Legal Clause Generation from Topics](https://aclanthology.org/2022.nllp-1.26) (Joshi et al., NLLP 2022)

• Graph-based Keyword Planning for Legal Clause Generation from Topics (Joshi et al., NLLP 2022)

ACL

• Sagar Joshi, Sumanth Balaji, Aparna Garimella, and Vasudeva Varma. 2022. Graph-based Keyword Planning for Legal Clause Generation from Topics. In Proceedings of the Natural Legal Language Processing Workshop 2022, pages 276–286, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.