Two main NLP components play a role in automating the creation of clinical documentation. The first component, Automatic Speech Recognition (ASR), is used to translate speech into text. It takes the audio recording of the encounter and generates a conversation transcription (cf. Figure 2). The second component, Automatic Text Summarization, helps generate summaries from large text documents. This component is responsible for understanding and capturing the nuances and most essential aspects from the transcribed conversation into a final report in narrative form (cf. Figure 3), structured form, or a combination of both.
We will focus on this second component, Automatic Text Summarization, which is a difficult task with many challenges:

Its performance is tied to the ASR quality from multiple speakers (noisy input).
The input is conversational in nature and contains layman's terms.
Protected Health Information (PHI) regulations limit medical data access.
