Natural Language Processing in Generating Industrial Documentation Within Industry 4.0/5.0

Deep learning (DL) methods have revolutionized natural language processing (NLP), enabling industrial documentation systems to process and generate text with high accuracy and fluency. Modern deep learning models, such as transformers and recurrent neural networks (RNNs), learn contextual relationships in text, making them ideal for analyzing and creating complex industrial documentation. Transformer-based architectures, such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), are ideally suited for tasks such as text summarization, content generation, and question answering, which are crucial for documentation systems. Pre-trained language models, tuned to specific industrial datasets, support domain-specific vocabulary, ensuring the generated documentation complies with industry standards. Deep learning-based systems can use sequential models, such as those used in machine translation, to generate documentation in multiple languages, promoting accessibility, and global collaboration. Using attention mechanisms, these models identify and highlight critical sections of input data, resulting in the generation of accurate and concise documentation. Integration with optical character recognition (OCR) tools enables DL-based NLP systems to digitize and interpret legacy documents, streamlining the transition to automated workflows. Reinforcement learning and human feedback loops can enhance a system’s ability to generate consistent and contextually relevant text over time. These approaches are particularly effective in creating dynamic documentation that is automatically updated based on data from sensors, registers, or other sources in real time. The scalability of DL techniques enables industrial organizations to efficiently produce massive amounts of documentation, reducing manual effort and improving overall efficiency. NLP has become a fundamental technology for automating the generation, maintenance, and personalization of industrial documentation within the Industry 4.0, 5.0, and emerging Industry 6.0 paradigms. Recent advances in large language models, search-assisted generation, and multimodal architectures have significantly improved the accuracy and contextualization of technical manuals, maintenance reports, and compliance documents. However, persistent challenges such as domain-specific terminology, data scarcity, and the risk of hallucinations highlight the limitations of current approaches in safety-critical manufacturing environments. This review synthesizes state-of-the-art methods, comparing rule-based, neural, and hybrid systems while assessing their effectiveness in addressing industrial requirements for reliability, traceability, and real-time adaptation. Human–AI collaboration and the integration of knowledge graphs are transforming documentation workflows as factories evolve toward cognitive and autonomous systems. The review included 32 articles published between 2018 and 2025. The implications of these bibliometric findings suggest that a high percentage of conference papers (69.6%) may indicate a field still in its conceptual phase, which contextualizes the article’s emphasis on proposed architecture rather than their industrial validation. Most research was conducted in computer science, suggesting early stages of technological maturity. The leading countries were China and India, but these countries did not have large publication counts, nor were leading researchers or affiliations observed, suggesting significant research dispersion. However, the most frequently observed SDGs indicate a clear health context, focusing on “industry innovation and infrastructure” and “good health and well-being”.