Summary of Query-guided Self-supervised Summarization Of Nursing Notes, by Ya Gao et al.
Query-Guided Self-Supervised Summarization of Nursing Notes
by Ya Gao, Hans Moen, Saila Koivusalo, Miika Koskinen, Pekka Marttinen
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A novel query-guided self-supervised domain adaptation approach is introduced for abstractive nursing note summarization, utilizing patient-related clinical queries as guidance without requiring reference summaries for training. The proposed method, QGSumm, is compared with state-of-the-art Large Language Models (LLMs) for nursing note summarization, showing GPT-4 to be competitive in maintaining original content and QGSumm achieving high-quality summaries with a good balance between recall and hallucination rate. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Nursing notes are important parts of Electronic Health Records. Summarizing these notes helps clinicians quickly understand patients’ conditions. Researchers have developed new ways to summarize notes, but they don’t work well without extra help. A team created a special approach that uses patient-related questions to guide the summarization process. This method doesn’t need any extra information to learn from. The researchers tested their approach and compared it with other methods using big language models. Their results show that this new approach can create good summaries by balancing what’s important and what’s not. |
Keywords
» Artificial intelligence » Domain adaptation » Gpt » Hallucination » Recall » Self supervised » Summarization
