@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 = "Proceedings of 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://preview.aclanthology.org/ingest-emnlp/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."
}