Summary of Optimizing Automatic Summarization Of Long Clinical Records Using Dynamic Context Extension:testing and Evaluation Of the Nbce Method, by Guoqing Zhang et al.
Optimizing Automatic Summarization of Long Clinical Records Using Dynamic Context Extension:Testing and Evaluation of the NBCE Method
by Guoqing Zhang, Keita Fukuyama, Kazumasa Kishimoto, Tomohiro Kuroda
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty Summary: The paper proposes an automatic method for summarizing patient clinical notes using large language models (LLMs). However, current LLMs struggle with context loss when processing long inputs, leading to reduced output quality. To address this issue, the authors use a 7B model, open-calm-7b, enhanced with Native Bayes Context Extend and a redesigned decoding mechanism that references one sentence at a time within a contextual window of 2048 tokens. The improved model achieves near parity with Google’s over 175B Gemini on ROUGE-L metrics with 200 samples, demonstrating strong performance using less resources and enhancing the feasibility of automated electronic medical record (EMR) summarization. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: Doctors spend a lot of time writing down patient notes. This takes away from other important tasks. To help them, researchers are working on ways to automatically summarize these notes. However, this can be tricky because computers have trouble understanding the context when given too much information at once. The authors of this paper developed a new method that uses computer models and tricks to keep track of the context while summarizing patient notes. They tested their approach and found it was almost as good as a more powerful model that required more resources. This is an important step towards making it easier for doctors to focus on what matters most. |
Keywords
» Artificial intelligence » Gemini » Rouge » Summarization