@inproceedings{saeed-2025-medifact,
    title = "Medifact at {P}er{A}ns{S}umm 2025: Leveraging Lightweight Models for Perspective-Specific Summarization of Clinical {Q}{\&}{A} Forums",
    author = "Saeed, Nadia",
    editor = "Ananiadou, Sophia  and
      Demner-Fushman, Dina  and
      Gupta, Deepak  and
      Thompson, Paul",
    booktitle = "Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2025.cl4health-1.29/",
    doi = "10.18653/v1/2025.cl4health-1.29",
    pages = "331--339",
    ISBN = "979-8-89176-238-1",
    abstract = "The PerAnsSumm 2025 challenge focuses on perspective-aware healthcare answer summarization (Agarwal et al., 2025). This work proposes a few-shot learning framework using a Snorkel-BART-SVM pipeline for classifying and summarizing open-ended healthcare community question-answering (CQA).An SVM model is trained with weak supervision via Snorkel, enhancing zero-shot learning. Extractive classification identifies perspective-relevant sentences, which are then summarized using a pretrained BART-CNN model. The approach achieved 12th place among 100 teams in the shared task, demonstrating computational efficiency and contextual accuracy. By leveraging pretrained summarization models, this work advances medical CQA research and contributes to clinical decision support systems."
}