Papers by Xavier Amatriain
Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures. (2020.findings-emnlp)
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| Challenge: | Summarization of medical conversations addresses a very real need in medical practice: capturing the most important aspects of a medical encounter. |
| Approach: | They propose a novel approach to medical conversation summarization that leverages the unique and independent local structures created when gathering a patient’s medical history. |
| Outcome: | The proposed model captures most or all of the information in 80% of the medical conversations making it a realistic alternative to costly manual summarization by medical experts. |
Injecting knowledge into language generation: a case study in auto-charting after-visit care instructions from medical dialogue (2023.acl-long)
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| Challenge: | Recent advances in language modeling have enabled applications across multiple domains such as education, jurisprudence, and healthcare. |
| Approach: | They propose a method to use knowledge to identify which rare words are important and uplift their conditional probability. |
| Outcome: | The proposed approach reduces the uncertainty of the model and improves factuality and coherence without negatively impacting fluency. |