Automatic Generation of Titles for Research Papers Using Language Models

• URL: http://arxiv.org/abs/2606.05085v1
• Date: Wed, 03 Jun 2026 16:49:57 GMT
• Title: Automatic Generation of Titles for Research Papers Using Language Models
• Abstract summary: We propose a technique to generate paper titles from abstracts using open-weight pre-trained and large language models.<n>We use the CSPubSum and LREC-COLING-2024 datasets and introduce a new dataset, SpringerSSAT, curated from four Springer journals in the social sciences.
• Score: 7.474480823192324
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The title of a research paper conveys its primary idea and, occasionally, its conclusions in a clear and concise manner. Choosing an appropriate title is often challenging, and automated title generation can assist authors in this task. In this work, we propose a technique to generate paper titles from abstracts using open-weight pre-trained and large language models. We use the CSPubSum and LREC-COLING-2024 datasets and introduce a new dataset, SpringerSSAT, curated from four Springer journals in the social sciences. Additionally, we use GPT-3.5-turbo in a zero-shot setting to generate titles. Model performance is evaluated with ROUGE, METEOR, MoverScore, BERTScore, and SciBERTScore metrics. Our experiments show that fine-tuned PEGASUS-large outperforms other models, including fine-tuned LLaMA-3-8B and zero-shot GPT-3.5-turbo, across most metrics. We further demonstrate that ChatGPT can generate creative paper titles. Overall, AI-generated titles are generally appropriate and reliable.

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