@inproceedings{marimuthu-krishnamurthy-2025-ltrc,
    title = "{LTRC}-{IIITH} at {P}er{A}ns{S}umm 2025: {S}pan{S}ense - Perspective-specific span identification and Summarization",
    author = "Marimuthu, Sushvin  and
      Krishnamurthy, Parameswari",
    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-1.37/",
    pages = "409--414",
    ISBN = "979-8-89176-238-1",
    abstract = "Healthcare community question-answering (CQA) forums have become popular for users seeking medical advice, offering answers that range from personal experiences to factual information. Traditionally, CQA summarization relies on the best-voted answer as a reference summary. However, this approach overlooks the diverse perspectives across multiple responses. Structuring summaries by perspective could better meet users' informational needs. The PerAnsSumm shared task addresses this by identifying and classifying perspective-specific spans (Task{\_}A) and generating perspective-specific summaries from question-answer threads (Task{\_}B). In this paper, we present our work on the PerAnsSumm shared task 2025 at the CL4Health Workshop, NAACL 2025. Our system leverages the RoBERTa-large model for identifying perspective-specific spans and the BART-large model for summarization. We achieved a Macro-F1 score of 0.9 (90{\%}) and a Weighted-F1 score of 0.92 (92{\%}) for classification. For span matching, our strict matching F1 score was 0.21 (21{\%}), while proportional matching reached 0.68 (68{\%}), resulting in an average Task A score of 0.6 (60{\%}). For Task B, we achieved a ROUGE-1 score of 0.4 (40{\%}), ROUGE-2 of 0.18 (18{\%}), and ROUGE-L of 0.36 (36{\%}). Additionally, we obtained a BERTScore of 0.84 (84{\%}), METEOR of 0.37 (37{\%}), and BLEU of 0.13 (13{\%}), resulting in an average Task B score of 0.38 (38{\%}). Combining both tasks, our system achieved an overall average score of 49{\%} and ranked 6th on the official leaderboard for the shared task."
}