Keywords-to-Title model for Automated Academic Title Generation

In an article, the title should cover the whole content with a few important words. Several automated title-generating tools are available in the Internet. This paper is aiming to design and implementation a new model to generate title using a list of keywords. The model using a new dataset that is generated from NIPS dataset with Configure training arguments. The proposed system preprocesses keyword data, trains on a curated dataset, and produces coherent, contextually relevant titles through controlled text generation. The proposed model shows a strong generative capability by accurately producing research titles from list of important keywords. Its efficient fine-tuning strategy enables high performance with minimal training resources. The experimental results show that the proposed fine-tuned T5 title generation model can produce titles that are very close to the original scientific titles. For the paper “Learning to Play the Game of Chess”, the generated title matched the original exactly. As a result, all evaluation metrics reached 1.0, including cosine similarity, ROUGE-L, BERT_F1, and SciBERT_F1, indicating complete lexical and semantic agreement between the generated and reference titles.