A Survey on Medical Document Summarization: From Machine Learning Techniques to Large Language Models
Fuente:
Wiley Open Access
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Artículo Open Access |
| Published: |
Wiley
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1867020744360198145 |
|---|---|
| author | Akash Ghosh Raghav Jain Anubhav Jhangra Sriparna Saha Adam Jatowt |
| author_facet | Akash Ghosh Raghav Jain Anubhav Jhangra Sriparna Saha Adam Jatowt Akash Ghosh Raghav Jain Anubhav Jhangra Sriparna Saha Adam Jatowt |
| collection | Wiley Open Access |
| contents | A Survey on Medical Document Summarization: From Machine Learning Techniques to Large Language Models Akash Ghosh Raghav Jain Anubhav Jhangra Sriparna Saha Adam Jatowt WIREs Data Mining and Knowledge Discovery ABSTRACT The widespread adoption of the Internet has transformed healthcare by enabling the digital storage, sharing, and management of medical documents. This shift has improved information access, enhanced patient care, and opened new avenues for research and innovation. As the volume of medical data available to clinicians and patients continues to grow, the need for effective summarization methods becomes increasingly critical. Recent breakthroughs in deep learning—particularly the emergence of Large Language Models (LLMs)—have further accelerated progress in this area. This paper provides a comprehensive survey of current techniques and emerging trends in medical document summarization. This article is categorized under: Algorithmic Development > Text Mining Application Areas > Health Care 10.1002/widm.70045 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/widm.70045 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_widm_70045 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | A Survey on Medical Document Summarization: From Machine Learning Techniques to Large Language Models Akash Ghosh Raghav Jain Anubhav Jhangra Sriparna Saha Adam Jatowt WIREs Data Mining and Knowledge Discovery A Survey on Medical Document Summarization: From Machine Learning Techniques to Large Language Models Akash Ghosh Raghav Jain Anubhav Jhangra Sriparna Saha Adam Jatowt WIREs Data Mining and Knowledge Discovery ABSTRACT The widespread adoption of the Internet has transformed healthcare by enabling the digital storage, sharing, and management of medical documents. This shift has improved information access, enhanced patient care, and opened new avenues for research and innovation. As the volume of medical data available to clinicians and patients continues to grow, the need for effective summarization methods becomes increasingly critical. Recent breakthroughs in deep learning—particularly the emergence of Large Language Models (LLMs)—have further accelerated progress in this area. This paper provides a comprehensive survey of current techniques and emerging trends in medical document summarization. This article is categorized under: Algorithmic Development > Text Mining Application Areas > Health Care 10.1002/widm.70045 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | A Survey on Medical Document Summarization: From Machine Learning Techniques to Large Language Models |
| topic | WIREs Data Mining and Knowledge Discovery |
| url | https://wires.onlinelibrary.wiley.com/doi/10.1002/widm.70045 |