@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T DataHacks at PerAnsSumm 2025: LoRA-Driven Prompt Engineering for Perspective Aware Span Identification and Summarization
%A Nawander, Vansh
%A Nerella, Chaithra Reddy
%Y Ananiadou, Sophia
%Y Demner-Fushman, Dina
%Y Gupta, Deepak
%Y Thompson, Paul
%S Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-238-1
%F nawander-nerella-2025-datahacks
%X 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.
%R 10.18653/v1/2025.cl4health-1.33
%U https://aclanthology.org/2025.cl4health-1.33/
%U https://doi.org/10.18653/v1/2025.cl4health-1.33
%P 374-379
Markdown (Informal)
[DataHacks at PerAnsSumm 2025: LoRA-Driven Prompt Engineering for Perspective Aware Span Identification and Summarization](https://aclanthology.org/2025.cl4health-1.33/) (Nawander & Nerella, CL4Health 2025)
ACL