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Effectiveness in Retrieving Legal Precedents

Bibliographic Details
Main Author: Mentzingen, Hugo
Publication Date: 2025
Other Authors: António, Nuno, Bação, Fernando
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/179711
Summary: Mentzingen, H., António, N., & Bação, F. (2025). Effectiveness in Retrieving Legal Precedents: Exploring Text Summarization and Cutting-Edge Language Models Toward a Cost-Efficient Approach. Artificial Intelligence and Law. https://doi.org/10.1007/s10506-025-09440-2 --- The authors acknowledge Brazil's Superintendency of Private Insurance for supporting and providing data for this work. This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS) (https://doi.org/10.54499/UIDB/04152/2020).
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spelling Effectiveness in Retrieving Legal PrecedentsExploring Text Summarization and Cutting-Edge Language Models Toward a Cost-Efficient ApproachTransfer learninglegal precedent retrievalTransformerlanguage modelcost-effectivenessLawArtificial IntelligenceSDG 16 - Peace, Justice and Strong InstitutionsMentzingen, H., António, N., & Bação, F. (2025). Effectiveness in Retrieving Legal Precedents: Exploring Text Summarization and Cutting-Edge Language Models Toward a Cost-Efficient Approach. Artificial Intelligence and Law. https://doi.org/10.1007/s10506-025-09440-2 --- The authors acknowledge Brazil's Superintendency of Private Insurance for supporting and providing data for this work. This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS) (https://doi.org/10.54499/UIDB/04152/2020).This study examines the interplay between text summarization techniques and embeddings from Language Models (LMs) in constructing expert systems dedicated to the retrieval of legal precedents, with an emphasis on achieving cost-efficiency. Grounded in the growing domain of Artificial Intelligence (AI) in law, our research confronts the perennial challenges of computational resource optimization and the reliability of precedent identification. Through Named Entity Recognition (NER) and part-of-speech (POS) tagging, we juxtapose various summarization methods to distill legal documents into a convenient form that retains their essence. We investigate the effectiveness of these methods in conjunction with state-of-the-art embeddings based on Large Language Models (LLMs), particularly ADA from OpenAI, which is trained on a wide range of general-purpose texts. Utilizing a dataset from one of Brazil’s administrative courts, we explore the efficacy of embeddings derived from a Transformer model tailored to legal corpora against those from ADA, gauging the impact of parameter size, training corpora, and context window on retrieving legal precedents. Our findings suggest that while the full text embedded with ADA’s extensive context window leads in retrieval performance, a balanced combination of POS-derived summaries and ADA embeddings presents a compelling trade-off between performance and resource expenditure, advocating for an efficient, scalable, intelligent system suitable for broad legal applications. This study contributes to the literature by delineating an optimal approach that harmonizes the dual imperatives of computational frugality and retrieval accuracy, propelling the legal field toward more strategic AI utilization.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNMentzingen, HugoAntónio, NunoBação, Fernando2025-02-24T23:04:16Z2025-02-202025-02-20T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article21application/pdfhttp://hdl.handle.net/10362/179711eng0924-8463PURE: 109233120https://doi.org/10.1007/s10506-025-09440-2info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-03-03T01:38:36Zoai:run.unl.pt:10362/179711Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T00:06:58.181447Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Effectiveness in Retrieving Legal Precedents
Exploring Text Summarization and Cutting-Edge Language Models Toward a Cost-Efficient Approach
title Effectiveness in Retrieving Legal Precedents
spellingShingle Effectiveness in Retrieving Legal Precedents
Mentzingen, Hugo
Transfer learning
legal precedent retrieval
Transformer
language model
cost-effectiveness
Law
Artificial Intelligence
SDG 16 - Peace, Justice and Strong Institutions
title_short Effectiveness in Retrieving Legal Precedents
title_full Effectiveness in Retrieving Legal Precedents
title_fullStr Effectiveness in Retrieving Legal Precedents
title_full_unstemmed Effectiveness in Retrieving Legal Precedents
title_sort Effectiveness in Retrieving Legal Precedents
author Mentzingen, Hugo
author_facet Mentzingen, Hugo
António, Nuno
Bação, Fernando
author_role author
author2 António, Nuno
Bação, Fernando
author2_role author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Mentzingen, Hugo
António, Nuno
Bação, Fernando
dc.subject.por.fl_str_mv Transfer learning
legal precedent retrieval
Transformer
language model
cost-effectiveness
Law
Artificial Intelligence
SDG 16 - Peace, Justice and Strong Institutions
topic Transfer learning
legal precedent retrieval
Transformer
language model
cost-effectiveness
Law
Artificial Intelligence
SDG 16 - Peace, Justice and Strong Institutions
description Mentzingen, H., António, N., & Bação, F. (2025). Effectiveness in Retrieving Legal Precedents: Exploring Text Summarization and Cutting-Edge Language Models Toward a Cost-Efficient Approach. Artificial Intelligence and Law. https://doi.org/10.1007/s10506-025-09440-2 --- The authors acknowledge Brazil's Superintendency of Private Insurance for supporting and providing data for this work. This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS) (https://doi.org/10.54499/UIDB/04152/2020).
publishDate 2025
dc.date.none.fl_str_mv 2025-02-24T23:04:16Z
2025-02-20
2025-02-20T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/179711
url http://hdl.handle.net/10362/179711
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0924-8463
PURE: 109233120
https://doi.org/10.1007/s10506-025-09440-2
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 21
application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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