Automated Title Generation for Scientific Papers Using NLP and Machine Learning

The task of generating appropriate and engaging research paper titles has gained significant attention with the rise of large-scale digital repositories and natural language processing (NLP) advancements. This study investigates the use of recurrent neural networks (RNNs) and deep learning techniques to automate the generation of scientific paper titles from abstract text, leveraging datasets. The research focuses on model design, training strategies, and data preprocessing techniques to effectively capture semantic and contextual information for accurate title prediction. Through a comparative evaluation of traditional RNN-based approaches and advanced sequence-to-sequence architectures, we analyze the models’ performance in terms of syntactic coherence, relevance, and fluency. Key challenges addressed include overfitting, data sparsity, and semantic drift between input abstracts and generated titles. The study highlights trade-offs between model complexity, training time, and output quality, offering insights into optimizing neural networks for title generation. Future research directions emphasize integrating transformer-based models, enhancing abstract-to-title alignment, and reducing dependence on large annotated datasets. The results contribute to the broader understanding of automated scientific writing tools and their applications in academic content generation and metadata enrichment.