Automatic Generation of Titles for Research Papers Using Language Models

2026-06-03

AI Summary

This study addresses the time-consuming and challenging task of academic paper title generation by proposing an automated approach that produces titles directly from abstracts. The authors introduce SpringerSSAT, a novel and publicly released dataset in the social sciences, and conduct a systematic evaluation of several large language models—including PEGASUS-large, LLaMA-3-8B, and GPT-3.5-turbo—for this task. Experimental results demonstrate that fine-tuned PEGASUS-large consistently outperforms other models across multiple metrics such as ROUGE, METEOR, and BERTScore, generating titles that are accurate, concise, and practically useful. The study also highlights the potential of ChatGPT in producing creative and engaging titles, suggesting complementary strengths among different model architectures for scholarly title generation.

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.

Problem

• title generation
• research papers
• language models
• abstract-to-title
• automated summarization

Innovation

• title generation
• large language models
• SpringerSSAT dataset
• PEGASUS-large
• zero-shot GPT