Summary of Medsumm: a Multimodal Approach to Summarizing Code-mixed Hindi-english Clinical Queries, by Akash Ghosh et al.
MedSumm: A Multimodal Approach to Summarizing Code-Mixed Hindi-English Clinical Queries
by Akash Ghosh, Arkadeep Acharya, Prince Jha, Aniket Gaudgaul, Rajdeep Majumdar, Sriparna Saha, Aman Chadha, Raghav Jain, Setu Sinha, Shivani Agarwal
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research tackles the crucial task of summarizing medical questions posed by patients in a multimodal setting, integrating visual cues to improve doctor-patient interactions and medical decision-making. The study introduces the Multimodal Medical Codemixed Question Summarization (MMCQS) dataset, combining Hindi-English codemixed medical queries with visual aids. This integration enhances the representation of a patient’s medical condition, providing a more comprehensive perspective. A framework named MedSumm is proposed, leveraging Large Language Models (LLMs) and Visual Language Models (VLMs). The study demonstrates the value of integrating visual information from images to improve summary creation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research aims to help doctors better understand patient questions by combining text and images. Currently, most studies focus on just text, ignoring important visual cues that can provide more information about a patient’s medical condition. This study introduces a new dataset that combines Hindi-English medical questions with images, which can improve summary creation. The researchers also propose a framework that uses special language models to create summaries. |
Keywords
» Artificial intelligence » Summarization
