Summary of Umedsum: a Unified Framework For Advancing Medical Abstractive Summarization, by Aishik Nagar et al.
uMedSum: A Unified Framework for Advancing Medical Abstractive Summarization
by Aishik Nagar, Yutong Liu, Andy T. Liu, Viktor Schlegel, Vijay Prakash Dwivedi, Arun-Kumar Kaliya-Perumal, Guna Pratheep Kalanchiam, Yili Tang, Robby T. Tan
First submitted to arxiv on: 22 Aug 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 The paper presents a comprehensive benchmark for advanced abstractive summarization methods in medicine, addressing the challenges of balancing faithfulness and informativeness. The authors propose a hybrid framework called uMedSum, which introduces novel approaches for removing confabulations and adding missing information. uMedSum outperforms previous state-of-the-art models by 11.8% on average, according to reference-free metrics, and doctors prefer its summaries six times more often than the previous SOTA in difficult cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical summarization is important for doctors, but current methods are limited because they either prioritize faithfulness or informativeness. The paper proposes a new way of doing this called uMedSum, which uses special techniques to remove mistakes and add missing information. This makes it better than previous models, which were not able to do these things as well. |
Keywords
* Artificial intelligence * Summarization
