Summary of Exploring Robustness in Doctor-patient Conversation Summarization: An Analysis Of Out-of-domain Soap Notes, by Yu-wen Chen et al.
Exploring Robustness in Doctor-Patient Conversation Summarization: An Analysis of Out-of-Domain SOAP Notes
by Yu-Wen Chen, Julia Hirschberg
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The study investigates the performance of state-of-the-art doctor-patient conversation generative summarization models on out-of-domain data. The authors evaluate two model configurations: one general model that generates summaries without specific notes, and another SOAP-oriented model that includes subjective (S), objective (O), assessment (A) and plan (P) notes. They analyze the limitations and strengths of fine-tuning language model-based methods and GPTs on both configurations, as well as conduct a Linguistic Inquiry and Word Count analysis to compare SOAP notes from different datasets. The results show a strong correlation for reference notes across different datasets, indicating that format mismatch is not the main cause of performance decline on out-of-domain data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how well AI models can summarize medical conversations between doctors and patients when they haven’t been trained on similar conversations before. Researchers compared two types of summarization models: one that generates summaries without specific notes, and another that includes sections for subjective information, objective facts, assessment, and planning. They tested these models using different language processing methods, such as fine-tuning language models and GPTs. The results show that the models can still generate good summaries even when they haven’t seen similar conversations before. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Summarization
