AUTOMATED ACADEMIC PAPER TITLE GENERATION USING A TRANSFORMER-BASED DEEP LEARNING MODEL

This study proposes the development of an automated academic paper title generation system using transformer-based deep learning. The approach leverages GPT-4, a large-scale pre-trained generative language model, as the core algorithm for summarization and title creation. Unlike traditional rule-based methods that rely on manual configuration, dependency trees, or keyword scoring, the proposed system minimizes human intervention by fine-tuning a pre-trained transformer model on domain-specific datasets. This enables the model to effectively capture semantic relationships between input text and suitable titles. By applying GPT-4, the system is expected to achieve notable improvements in fluency, semantic relevance, and readability, with projected human evaluation scores ranging from 4.5 to 4.9 across metrics surpassing the 4.0 to 4.5 averages of existing models such as DTATG. The study finding out that Transformers have significantly advanced natural language processing capabilities, offering powerful tools for understanding, generating, and classifying academic paper titles. Their ability to capture contextual information and semantic nuances makes them ideal for automating tasks such as title suggestion, classification, and summarization within scholarly publishing. Fine-tuning these models on domain-specific datasets enhances their accuracy and relevance, enabling more efficient management of large academic corpora. Overall, transformer-based models provide a robust framework that improves the automation and precision of handling academic titles. The result therefore concluded that transformer models represent a transformative step forward in processing and generating academic paper titles. Their exceptional ability to understand complex scientific language ensures high performance across various applications, including automated title creation and classification. The study recommends that academic institutions and publishers adopt transformer-based systems tailored to their needs and explore hybrid approaches that integrate other natural language processing techniques. Finally, attention should be given to rigorous model evaluation and ethical considerations, ensuring transparency, fairness, and reliability in automated title generation.