Abstract: Transcript generation as part of automatic speech recognition may be guided using section types. Audio data is received for transcription. An initial transcript of the audio data may be generated and evaluated to determine a section type for the audio data. The section type may then be used to focus generation of a second version of the transcript on one speaker over another speaker.

Abstract: A medical audio summarization service receives a medical conversation and an indication of a user preferred summarization style selected from a plurality of available summarization styles to generate a medical summary that conforms to the user preferred summarization style. A transcript is generated via a medical audio transcription service, and the transcript is used by a natural language processing engine (including a large language model) to generate the medical summary. The large language model is trained to be used to generate medical summaries that conform to respective ones of a plurality of user preferred summarization styles. The large language model is trained using training data comprising previously generated summaries and summary interaction metadata generated from user edits and/or feedback.