@inproceedings{cuadron-cortes-etal-2025-arghitz,
    title = "{A}rg{H}i{TZ} at {A}rch{EHR}-{QA} 2025: A Two-Step Divide and Conquer Approach to Patient Question Answering for Top Factuality",
    author = "Cuadron Cortes, Adrian  and
      Sagasti, Aimar  and
      Urruela, Maitane  and
      De La Iglesia, Iker  and
      Garc{\'i}a Domingo-aldama, Ane  and
      Atutxa Salazar, Aitziber  and
      Goikoetxea, Josu  and
      Barrena, Ander",
    editor = "Soni, Sarvesh  and
      Demner-Fushman, Dina",
    booktitle = "BioNLP 2025 Shared Tasks",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bionlp-share.1/",
    pages = "1--10",
    ISBN = "979-8-89176-276-3",
    abstract = "This work presents three different approaches to address the ArchEHR-QA 2025 Shared Task on automated patient question answering. We introduce an end-to-end prompt-based baseline and two two-step methods to divide the task, without utilizing any external knowledge. Both two step approaches first extract essential sentences from the clinical text{---}by prompt or similarity ranking{---}, and then generate the final answer from these notes. Results indicate that the re-ranker based two-step system performs best, highlighting the importance of selecting the right approach for each subtask. Our best run achieved an overall score of 0.44, ranking 8th out of 30 on the leaderboard, securing the top position in overall factuality."
}