# Enhancing Biomedical Text Summarization and Question-Answering: On the Utility of Domain-Specific Pre-Training

Source: [Enhancing Biomedical Text Summarization and Question-Answering: On the Utility of Domain-Specific Pre-Training](<https://www.sota2.com/research/paper/68fa6ffd1e209131cea485a9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:30:17.231Z

Authors: Dima Galat, Marian-Andrei Rizoiu

Published: 2023-07-10T08:32:45.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2307.04412>)

[PDF](<https://arxiv.org/pdf/2307.04412.pdf>)