arXiv:2606.05085v1 Announce Type: cross 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
The proliferation of powerful large language models makes automated text generation tasks more feasible and effective than ever before.
Automated title generation can significantly enhance the efficiency of academic publishing and information retrieval, impacting research dissemination and discovery.
Researchers may see AI tools becoming more integrated into their writing processes, potentially reducing the cognitive load of crafting effective paper titles.
- · Scientific researchers
- · Academic publishers
- · NLP researchers
- · AI tool developers
- · Manual abstract/title drafting services
Increased efficiency in academic paper preparation and submission.
Improved discoverability and citation rates for research papers due to more optimized titles.
Potential for an exponential increase in published research, requiring new methods for quality control and information synthesis.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI