@inproceedings{r-etal-2025-aicoe,
    title = "{AICOE} at {P}er{A}ns{S}umm 2025: An Ensemble of Large Language Models for Perspective-Aware Healthcare Answer Summarization",
    author = "R, Rakshith  and
      Khan, Mohammed Sameer  and
      Chopra, Ankush",
    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/fix-sig-urls/2025.cl4health-1.36/",
    pages = "398--408",
    ISBN = "979-8-89176-238-1",
    abstract = "The PerAnsSumm 2024 shared task at the CL4Health workshop focuses on generating structured, perspective-specific summaries to enhance the accessibility of health-related information. Given a Healthcare community QA dataset containing a question, context, and multiple user-answers, the task involves identifying relevant perspective categories, extracting spans from these perspectives, and generating concise summaries for the extracted spans. We fine-tuned open-source models such as Llama-3.2 3B, Llama-3.1 8B, and Gemma-2 9B, while also experimenting with proprietary models including GPT-4o, o1, Gemini-1.5 Pro, and Gemini-2 Flash Experimental using few-shot prompting. Our best-performing approach leveraged an ensemble strategy, combining span outputs from o1 (CoT) and Gemini-2 Flash Experimental. For overlapping perspectives, we prioritized Gemini. The final spans were summarized using Gemini, preserving the higher classification accuracy of o1 while leveraging Gemini{'}s superior span extraction and summarization capabilities. This hybrid method secured fourth place on the final leaderboard among 100 participants and 206 submissions."
}