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Improving Large Language Models’ Summarization Accuracy by Adding Highlights to Discharge Notes: Comparative Evaluation

Improving Large Language Models’ Summarization Accuracy by Adding Highlights to Discharge Notes: Comparative Evaluation

Their method outperformed 3 baseline approaches, namely frequency-based, graph-based, and K-means centroid-based extractive summarization, demonstrating better content preservation and semantic alignment with the original notes. In the study by Alsentzer and Kim [38], they explored extractive summarization of discharge notes from the MIMIC-III database.

Mahshad Koohi Habibi Dehkordi, Yehoshua Perl, Fadi P Deek, Zhe He, Vipina K Keloth, Hao Liu, Gai Elhanan, Andrew J Einstein

JMIR Med Inform 2025;13:e66476