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        <title>Abdelmalak at PerAnsSumm 2025: Leveraging a Domain-Specific BERT and LLaMA for Perspective-Aware Healthcare Answer Summarization</title>
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            <title>Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health)</title>
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            <namePart type="given">Sophia</namePart>
            <namePart type="family">Ananiadou</namePart>
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        <name type="personal">
            <namePart type="given">Dina</namePart>
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    <abstract>The PerAnsSumm Shared Task - CL4Health@NAACL 2025 aims to enhance healthcare community question-answering (CQA) by summarizing diverse user perspectives. It consists of two tasks: identifying and classifying perspective-specific spans (Task A) and generating structured, perspective-specific summaries from question-answer threads (Task B). The dataset used for this task is the PUMA dataset. For Task A, a COVID-Twitter-BERT model pre-trained on COVID-related text from Twitter was employed, improving the model’s understanding of relevant vocabulary and context. For Task B, LLaMA was utilized in a prompt-based fashion. The proposed approach achieved 9th place in Task A and 16th place overall, with the best proportional classification F1-score of 0.74.</abstract>
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%0 Conference Proceedings
%T Abdelmalak at PerAnsSumm 2025: Leveraging a Domain-Specific BERT and LLaMA for Perspective-Aware Healthcare Answer Summarization
%A Abdelmalak, Abanoub
%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 abdelmalak-2025-abdelmalak
%X The PerAnsSumm Shared Task - CL4Health@NAACL 2025 aims to enhance healthcare community question-answering (CQA) by summarizing diverse user perspectives. It consists of two tasks: identifying and classifying perspective-specific spans (Task A) and generating structured, perspective-specific summaries from question-answer threads (Task B). The dataset used for this task is the PUMA dataset. For Task A, a COVID-Twitter-BERT model pre-trained on COVID-related text from Twitter was employed, improving the model’s understanding of relevant vocabulary and context. For Task B, LLaMA was utilized in a prompt-based fashion. The proposed approach achieved 9th place in Task A and 16th place overall, with the best proportional classification F1-score of 0.74.
%R 10.18653/v1/2025.cl4health-1.39
%U https://aclanthology.org/2025.cl4health-1.39/
%U https://doi.org/10.18653/v1/2025.cl4health-1.39
%P 428-436

Markdown (Informal)

[Abdelmalak at PerAnsSumm 2025: Leveraging a Domain-Specific BERT and LLaMA for Perspective-Aware Healthcare Answer Summarization](https://aclanthology.org/2025.cl4health-1.39/) (Abdelmalak, CL4Health 2025)

• Abdelmalak at PerAnsSumm 2025: Leveraging a Domain-Specific BERT and LLaMA for Perspective-Aware Healthcare Answer Summarization (Abdelmalak, CL4Health 2025)

ACL

• Abanoub Abdelmalak. 2025. Abdelmalak at PerAnsSumm 2025: Leveraging a Domain-Specific BERT and LLaMA for Perspective-Aware Healthcare Answer Summarization. In Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health), pages 428–436, Albuquerque, New Mexico. Association for Computational Linguistics.