Empowering legal justice with AI: A reinforcement learning SAC-VAE framework for advanced legal text summarization

Automated summarization of legal texts poses a significant challenge due to the complex and specialized nature of legal documentation. Despite the recent progress in reinforcement learning for natural language text summarization, its application in the legal domain has been less effective. This pape...

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Bibliographic Details
Published in:PloS one 2024-10, Vol.19 (10), p.e0312623
Main Authors: Wang, Xukang, Wu, Ying Cheng
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Automation
Computational linguistics
Datasets
Deep learning
Effectiveness
Electronic data processing
Encoders
Humans
Justice, Administration of
Language processing
Learning
Legal documents
Legal research
Machine learning
Methods
Natural language interfaces
Natural Language Processing
Reinforcement
Reinforcement learning (Machine learning)
Texts
United States
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Summary:Automated summarization of legal texts poses a significant challenge due to the complex and specialized nature of legal documentation. Despite the recent progress in reinforcement learning for natural language text summarization, its application in the legal domain has been less effective. This paper introduces SAC-VAE, a novel reinforcement learning framework specifically designed for legal text summarization. We leverage a Variational Autoencoder (VAE) to condense the high-dimensional state space into a more manageable lower-dimensional feature space. These compressed features are subsequently utilized by the Soft Actor-Critic (SAC) algorithm for policy learning, facilitating the automated generation of summaries from legal texts. Through comprehensive experimentation, we have empirically demonstrated the effectiveness and superior performance of the SAC-VAE framework in legal text summarization.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0312623