before_sent:  In this paper, we present the first study to evaluate complete pipelines for leveraging these transcripts to train machine learning model to  generate these notes . We first describe a unique dataset of patient visit records, consisting of transcripts , paired SOAP notes, and annotations marking noteworthy utterances that support each summary sentence.
before_sent_with_intent: <clarity>  In this paper, we present the first study to evaluate complete pipelines for leveraging these transcripts to train machine learning model to  generate these notes . We first describe a unique dataset of patient visit records, consisting of transcripts , paired SOAP notes, and annotations marking noteworthy utterances that support each summary sentence.
after_sent:  In this paper, we present the first study to evaluate complete pipelines for leveraging these transcripts to train machine learning model to generate these notes from conversations between physicians and patients. We benefit from a dataset that, along with transcripts and paired SOAP notes, and annotations marking noteworthy utterances that support each summary sentence.