Enhancing Biomedical Text Summarization and Question-Answering: On the Utility of Domain-Specific Pre-Training
This addresses the problem of limited data for biomedical text generation, offering a more efficient approach for researchers and practitioners in the biomedical domain, though it is incremental as it builds on existing transfer learning methods.
The paper tackled the challenge of biomedical text summarization by showing that general-domain pre-training followed by task-specific fine-tuning outperforms domain-specific pre-training on a BioASQ task, achieving competitive results with only a thousand in-domain examples.
Biomedical summarization requires large datasets to train for text generation. We show that while transfer learning offers a viable option for addressing this challenge, an in-domain pre-training does not always offer advantages in a BioASQ summarization task. We identify a suitable model architecture and use it to show a benefit of a general-domain pre-training followed by a task-specific fine-tuning in the context of a BioASQ summarization task, leading to a novel three-step fine-tuning approach that works with only a thousand in-domain examples. Our results indicate that a Large Language Model without domain-specific pre-training can have a significant edge in some domain-specific biomedical text generation tasks.