Unified Text Segmentation (TSEG) Deep Learning Function for Legal Text Recovery: Enhancing Generative-NLP and AI-Driven Regulatory Operations in Industry 5.0

Purpose: The emergence of Industry 5.0 demands intelligent systems that not only automate tasks but also collaborate meaningfully with humans, especially in highly regulated fields like law and governance. This study addresses the challenges of fragmented, redacted, and corrupted legal documents, which hinder compliance, auditing, and interpretability.. Design/Methodology/Approach: This research introduces a Unified Tseg Deep Learning Function—a multi-layered framework combining legal text segmentation (Tseg), generative NLP techniques, and legal ontology alignment. The model incorporates transformer architectures, attention mechanisms, and recursive state modeling to enable dynamic content recovery, contextual classification, and visibility-based annotation based on age and jurisdiction. Findings: The proposed system effectively recovers missing or corrupted legal content, categorizes segments by legal domain, and applies jurisdiction-specific and age-based visibility rules. It demonstrates strong potential in automating and enhancing interpretability within legal workflows and regulatory compliance systems. Practical Implications: This framework can support digital governance platforms, legal information retrieval systems, and compliance monitoring tools by enabling more accurate and automated reconstruction and classification of legal documents, thus improving transparency and accountability in legal processes Originality/Value: The study offers a novel integration of Tseg, generative NLP, and legal ontologies tailored for Industry 5.0. Its unique contribution lies in bridging technical deep learning methods with domain-specific legal compliance requirements, supporting the evolution of intelligent, human-centric regulatory technologies.