@inproceedings{zaid-etal-2025-multilingual,
title = "Multilingual Clinical Dialogue Summarization and Information Extraction with Qwen-1.5{B} {L}o{RA}",
author = "Zaid, Kunwar and
Sangroya, Amit and
Khatri, Jyotsana",
editor = "Krishnamurthy, Parameswari and
Mujadia, Vandan and
Misra Sharma, Dipti and
Mary Thomas, Hannah",
booktitle = "NLP-AI4Health",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.nlpai4health-main.6/",
pages = "69--74",
ISBN = "979-8-89176-315-9",
abstract = "This paper describes our submission to theNLP-AI4Health 2025 Shared Task on multi-lingual clinical dialogue summarization andstructured information extraction. Our systemis based on Qwen-1.5B Instruct fine-tuned withLoRA adapters for parameter-efficient adapta-tion. The pipeline produces (i) concise Englishsummaries, (ii) schema-aligned JSON outputs,and (iii) multilingual Q{\&}A responses. TheQwen-based approach substantially improvessummary fluency, factual completeness, andJSON field coverage while maintaining effi-ciency within constrained GPU resources."
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<abstract>This paper describes our submission to theNLP-AI4Health 2025 Shared Task on multi-lingual clinical dialogue summarization andstructured information extraction. Our systemis based on Qwen-1.5B Instruct fine-tuned withLoRA adapters for parameter-efficient adapta-tion. The pipeline produces (i) concise Englishsummaries, (ii) schema-aligned JSON outputs,and (iii) multilingual Q&A responses. TheQwen-based approach substantially improvessummary fluency, factual completeness, andJSON field coverage while maintaining effi-ciency within constrained GPU resources.</abstract>
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%0 Conference Proceedings
%T Multilingual Clinical Dialogue Summarization and Information Extraction with Qwen-1.5B LoRA
%A Zaid, Kunwar
%A Sangroya, Amit
%A Khatri, Jyotsana
%Y Krishnamurthy, Parameswari
%Y Mujadia, Vandan
%Y Misra Sharma, Dipti
%Y Mary Thomas, Hannah
%S NLP-AI4Health
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-315-9
%F zaid-etal-2025-multilingual
%X This paper describes our submission to theNLP-AI4Health 2025 Shared Task on multi-lingual clinical dialogue summarization andstructured information extraction. Our systemis based on Qwen-1.5B Instruct fine-tuned withLoRA adapters for parameter-efficient adapta-tion. The pipeline produces (i) concise Englishsummaries, (ii) schema-aligned JSON outputs,and (iii) multilingual Q&A responses. TheQwen-based approach substantially improvessummary fluency, factual completeness, andJSON field coverage while maintaining effi-ciency within constrained GPU resources.
%U https://aclanthology.org/2025.nlpai4health-main.6/
%P 69-74
Markdown (Informal)
[Multilingual Clinical Dialogue Summarization and Information Extraction with Qwen-1.5B LoRA](https://aclanthology.org/2025.nlpai4health-main.6/) (Zaid et al., NLP-AI4Health 2025)
ACL