@inproceedings{nawander-nerella-2025-datahacks,
    title = "{D}ata{H}acks at {P}er{A}ns{S}umm 2025: {L}o{RA}-Driven Prompt Engineering for Perspective Aware Span Identification and Summarization",
    author = "Nawander, Vansh  and
      Nerella, Chaithra Reddy",
    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/ingest-emnlp/2025.cl4health-1.33/",
    doi = "10.18653/v1/2025.cl4health-1.33",
    pages = "374--379",
    ISBN = "979-8-89176-238-1",
    abstract = "This paper presents the approach of the DataHacks team in the PerAnsSumm Shared Task at CL4Health 2025, which focuses on perspective-aware summarization of healthcare community question-answering (CQA) forums. Unlike traditional CQA summarization, which relies on the best-voted answer, this task captures diverse perspectives, including `cause,' `suggestion,' `experience,' `question,' and `information.' The task is divided into two subtasks: (1) identifying and classifying perspective-specific spans, and (2) generating perspective-specific summaries. We addressed these tasks using Large Language Models (LLM), fine-tuning it with different low-rank adaptation (LoRA) configurations to balance performance and computational efficiency under resource constraints. In addition, we experimented with various prompt strategies and analyzed their impact on performance. Our approach achieved a combined average score of 0.42, demonstrating the effectiveness of fine-tuned LLMs with adaptive LoRA configurations for perspective-aware summarization."
}