Development of a Content Creation Model Using Natural Language Generation - Paul-Odeli Jonathan Ateko; Constance I. Amannah; Henry Onyebuchukwu Ordu; Emmanuel J. Izionworu (JOMLAI: Journal of Machine Learning and Artificial Intelligence, 2026)
The increasing demand for scalable, high-quality digital content has exposed the limitations of manual content creation and existing Natural Language Generation (NLG) systems, particularly in terms of domain specificity, ethical reliability, and readiness for optimization. This study addresses this gap by developing NLG-ACCO, a transformer-based model for automated content creation and optimization in educational, media, and digital marketing applications. Transformer-XL was selected over newer architectures like Llama-3 or Mistral because it models longer contextual dependencies beyond fixed-length segments—essential for coherent paragraph-level content—while offering a better trade-off between performance, computational efficiency, and transparency under resource-constrained conditions. The model integrates domain-aware fine-tuning, reinforcement learning, SEO optimization, and ethical safeguards, including bias detection and factual verification. Evaluation used BLEU, ROUGE, readability indices, and Perplexity. NLG-ACCO achieved a BLEU score of 0.79 (baseline: 0.61) and ROUGE-L of 0.76 (baseline: 0.36). Perplexity dropped from 45.2 to 27.8, indicating more coherent predictions. Readability improved by 24%, post-editing time decreased by 38.5%, and bias detection mitigated 87% of flagged cases. These results demonstrate that integrating optimization and ethical controls within Transformer-XL frameworks significantly enhances content quality and reliability.