Before revision:  Recent advancements in automatic speech recognition (ASR) and natural language understanding (NLU) offer potential solutions to generate these summaries automatically . In the current paper, we focus on two tasks: classifying utterances from medical conversations according to (i) the SOAP section and (ii) the speaker role , both fundamental building blocks along the path towards an end-to-end, automated SOAP note for medical conversations.
After revision:  Recent advancements in automatic speech recognition (ASR) and natural language understanding (NLU) offer potential solutions to generate these summaries automatically , but rigorous quantitative baselines for benchmarking research in this domain are lacking. In this paper, we focus on two tasks: classifying utterances from medical conversations according to (i) the SOAP section and (ii) the speaker role , both fundamental building blocks along the path towards an end-to-end, automated SOAP note for medical conversations.