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The goal of automated summarization techniques (Paice, 1990; Kupiec et al,1995) is to condense text by focusing on the most critical information.Generative large language models (LLMs) have shown to be robust summarizers,yet traditional metrics struggle to capture resulting performance (Goyal et al,2022) in more powerful LLMs. In safety-critical domains such as medicine, morerigorous evaluation is required, especially given the potential for LLMs toomit important information in the resulting summary. We propose MED-OMIT, a newomission benchmark for medical summarization. Given a doctor-patientconversation and a generated summary, MED-OMIT categorizes the chat into a setof facts and identifies which are omitted from the summary. We further proposeto determine fact importance by simulating the impact of each fact on adownstream clinical task: differential diagnosis (DDx) generation. MED-OMITleverages LLM prompt-based approaches which categorize the importance of factsand cluster them as supporting or negating evidence to the diagnosis. Weevaluate MED-OMIT on a publicly-released dataset of patient-doctorconversations and find that MED-OMIT captures omissions better than alternativemetrics.

Extrinsically-Focused Evaluation of Omissions in Medical Summarization