@inproceedings{sim-etal-2023-csiro,
    title = "{CSIRO} {D}ata61 Team at {B}io{L}ay{S}umm Task 1: Lay Summarisation of Biomedical Research Articles Using Generative Models",
    author = "Sim, Mong Yuan  and
      Dai, Xiang  and
      Rybinski, Maciej  and
      Karimi, Sarvnaz",
    editor = "Demner-fushman, Dina  and
      Ananiadou, Sophia  and
      Cohen, Kevin",
    booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.bionlp-1.68",
    doi = "10.18653/v1/2023.bionlp-1.68",
    pages = "629--635",
    abstract = "Lay summarisation aims at generating a summary for non-expert audience which allows them to keep updated with latest research in a specific field. Despite the significant advancements made in the field of text summarisation, lay summarisation remains relatively under-explored. We present a comprehensive set of experiments and analysis to investigate the effectiveness of existing pre-trained language models in generating lay summaries. When evaluate our models using a BioNLP Shared Task, BioLaySumm, our submission ranked second for the relevance criteria and third overall among 21 competing teams.",
}

Lay summarisation aims at generating a summary for non-expert audience which allows them to keep updated with latest research in a specific field. Despite the significant advancements made in the field of text summarisation, lay summarisation remains relatively under-explored. We present a comprehensive set of experiments and analysis to investigate the effectiveness of existing pre-trained language models in generating lay summaries. When evaluate our models using a BioNLP Shared Task, BioLaySumm, our submission ranked second for the relevance criteria and third overall among 21 competing teams.
Sim, Mong Yuan Dai, Xiang Rybinski, Maciej Karimi, Sarvnaz
CSIRO Data61 Team at BioLaySumm Task 1: Lay Summarisation of Biomedical Research Articles Using Generative Models