Automated title generation for research papers: A transformer-based approach
| dc.contributor.author | Hosseinzadeh, Maryam | |
| dc.contributor.supervisor | Baniasadi, Amirali | |
| dc.date.accessioned | 2024-09-04T22:09:10Z | |
| dc.date.available | 2024-09-04T22:09:10Z | |
| dc.date.issued | 2024 | |
| dc.degree.department | Department of Electrical and Computer Engineering | |
| dc.degree.level | Master of Engineering MEng | |
| dc.description.abstract | The task of generating concise, informative, and relevant titles for research papers is a critical but challenging aspect of academic publishing. This project report explores the application of Transformer-based Large Language Models (LLMs) to automatically generate and suggest research paper titles from their abstracts. Building on the foundational Transformer architecture introduced by Vaswani et al., we evaluate the performance of different models, including a custom-built LLM, a pre-trained GPT-2 model, and a fine-tuned version of GPT-2. Through qualitative analysis, we demonstrate that fine-tuning GPT-2 on a specific dataset of research paper abstracts and titles significantly enhances the coherence, relevance, and contextual accuracy of the generated titles. We address challenges such as hallucinations in LLM-generated text and discuss the importance of high-quality datasets and task-specific fine-tuning. This work contributes to the broader understanding of the capabilities and limitations of LLMs in specialized NLP tasks, offering insights for future research and applications in academic publishing. | |
| dc.description.scholarlevel | Graduate | |
| dc.identifier.uri | https://hdl.handle.net/1828/20375 | |
| dc.language.iso | en | |
| dc.rights | Available to the World Wide Web | |
| dc.subject | transformers | |
| dc.subject | large language models | |
| dc.subject | natural language processing | |
| dc.title | Automated title generation for research papers: A transformer-based approach | |
| dc.type | project |