Can pre-trained language models generate titles for research papers?
This addresses the arduous task of title creation for authors, offering an incremental improvement in automated title generation.
The paper tackled the problem of automating research paper title generation by fine-tuning pre-trained language models like PEGASUS-large and using GPT-3.5-turbo in a zero-shot setting, finding that fine-tuned PEGASUS-large outperformed other models across most metrics such as ROUGE and BERTScore.
The title of a research paper communicates in a succinct style the main theme and, sometimes, the findings of the paper. Coming up with the right title is often an arduous task, and therefore, it would be beneficial to authors if title generation can be automated. In this paper, we fine-tune pre-trained language models to generate titles of papers from their abstracts. Additionally, we use GPT-3.5-turbo in a zero-shot setting to generate paper titles. The performance of the models is measured with ROUGE, METEOR, MoverScore, BERTScore and SciBERTScore metrics. We find that fine-tuned PEGASUS-large outperforms the other models, including fine-tuned LLaMA-3-8B and GPT-3.5-turbo, across most metrics. We also demonstrate that ChatGPT can generate creative titles for papers. Our observations suggest that AI-generated paper titles are generally accurate and appropriate.