@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://preview.aclanthology.org/ingest-ijcnlp-aacl/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."
}