@inproceedings{roy-das-2025-nlp4health,
    title = "{NLP}4{H}ealth: Multilingual Clinical Dialogue Summarization and {QA} with m{T}5 and {L}o{RA}",
    author = "Roy, Moutushi  and
      Das, Dipankar",
    editor = "Zechariah, Arun  and
      Krishna S, Balu  and
      Misra Sharma, Dipti  and
      Mary Thomas, Hannah  and
      Mammen, Joy  and
      Krishnamurthy, Parameswari  and
      Mujadia, Vandan",
    booktitle = "NLP-AI4Health",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/master-new-author-system-docs/2025.nlpai4health-main.10/",
    pages = "93--97",
    ISBN = "979-8-89176-315-9",
    abstract = "In this work, we present NLP4Health, a unified and reproducible pipeline to accomplish the tasks of multilingual clinical dialogue summarization and question answering (QA). Our system fine-tunes the multilingual sequence-to-sequence model google/mt5-base along with parameter-efficient Low-Rank Adaptation (LoRA) modules to support ten Indian languages. For each clinical dialogue, the model produces (1) a free-text English summary, (2) an English structured key{--}value (KnV) JSON summary, and (3) QA responses in the dialogue{'}s original language. We conducted preprocessing, fine-tuning, and inference, and evaluated across QA, textual, and structured metrics, analyzing performance in low-resource settings. The adapter weights, tokenizer, and inference scripts are publicly released to promote transparency and reproducibility."
}