Automatic Generation of Titles for Research Papers Using Language Models

Researchers propose an automated technique for generating research paper titles from abstracts using large language models, testing multiple approaches including fine-tuned PEGASUS and zero-shot GPT-3.5-turbo. Fine-tuned PEGASUS-large emerges as the top performer, though ChatGPT demonstrates creative title generation capabilities, suggesting AI-generated titles are practical and reliable for academic publishing workflows.

This research addresses a genuine pain point in academic publishing where title selection significantly impacts paper discoverability and citation rates. The study evaluates multiple state-of-the-art language models across diverse evaluation metrics, providing a comprehensive empirical foundation for practitioners seeking to automate title generation. The introduction of SpringerSSAT, a new dataset from Springer journals, contributes a valuable resource for future research while demonstrating that models perform differently across disciplinary contexts in the social sciences.

Automated title generation sits within the broader trend of AI-assisted academic workflows. Over the past two years, large language models have progressively integrated into research processes—from literature review automation to manuscript writing assistance. This development reflects institutions' efforts to improve researcher productivity amid growing publication volumes and competitive pressures.

For academic publishers, research institutions, and journals, these findings suggest viable pathways to implement quality-assurance tools that help authors craft more effective titles while reducing editorial overhead. The performance gap between fine-tuned models and zero-shot approaches indicates that publishers investing in domain-specific model tuning can achieve better results than relying solely on general-purpose APIs. ChatGPT's capability for creative generation opens possibilities for exploring alternative title styles that might improve reader engagement.

Future development should focus on cross-disciplinary model robustness and integration with existing manuscript submission systems. Researchers should monitor whether AI-generated titles impact citation patterns or reader engagement differently than human-authored ones, as this determines practical adoption value across publishing ecosystems.

• →Fine-tuned PEGASUS-large outperforms other models including LLaMA-3-8B and zero-shot GPT-3.5-turbo for automated academic title generation.
• →A new SpringerSSAT dataset curated from social science journals provides domain-specific training resources for title generation research.
• →Multiple evaluation metrics (ROUGE, METEOR, BERTScore, SciBERTScore) demonstrate consistent performance validation across different assessment approaches.
• →ChatGPT shows creative title generation capabilities despite zero-shot limitations in structured evaluation metrics.
• →AI-generated titles are demonstrated to be generally appropriate and reliable for academic publishing applications.