@inproceedings{toney-etal-2025-expertly,
    title = "Expertly Informed, Generatively Summarized: A Hybrid {RAG} Approach to Informed Consent Summarization with Auxiliary Expert Knowledge",
    author = "Toney-Wails, Autumn  and
      Wails, Ryan  and
      Smith, Caleb",
    editor = "Shi, Weijia  and
      Yu, Wenhao  and
      Asai, Akari  and
      Jiang, Meng  and
      Durrett, Greg  and
      Hajishirzi, Hannaneh  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 4th International Workshop on Knowledge-Augmented Methods for Natural Language Processing",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.knowledgenlp-1.23/",
    pages = "246--258",
    ISBN = "979-8-89176-229-9",
    abstract = "The utility of retrieval augmented generation (RAG) systems is actively being explored across a wide range of domains. Reliable generative output is increasingly useful in fields where routine tasks can be streamlined and potentially improved by integrating domain-specific data in addition to individual expert knowledge, such as medical care. To that end, we present a hybrid RAG and GraphRAG user interface system to summarize the key information (KI) section in IRB informed consent documents. KI summaries are a unique task, as generative summarization helps the end user (clinical trial expert) but can pose a risk to the affected user (potential study participants) if inaccurately constructed. Thus, the KI summarization task requires reliable, structured output with input from an expert knowledge source outside of the informed consent document. Reviewed by IRB domain experts and clinical trial PIs, our summarization application produces accurate (70{\%} to 100{\%} varied by accuracy type) and useful summaries (63{\%} of PIs stating summaries were as good as or better than their accepted summaries)."
}