Abstract
As part of our participation in BioLaySumm 2023, we explored the use of large language models (LLMs) to automatically generate concise and readable summaries of biomedical research articles. We utilized pre-trained LLMs to fine-tune our summarization models on two provided datasets, and adapt them to the shared task within the constraints of training time and computational power. Our final models achieved very high relevance and factuality scores on the test set, and ranked among the top five models in the overall performance.
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CITATION STYLE
Liu, Q., Ren, X., & Vydiswaran, V. G. V. (2023). LHS712EE at BioLaySumm 2023: Using BART and LED to summarize biomedical research articles. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 620–624). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.bionlp-1.66
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