7 papers with code • 0 benchmarks • 2 datasets
Generating a summary from meeting transcriptions.
Leverage Unlabeled Data for Abstractive Speech Summarization with Self-Supervised Learning and Back-Summarization
In order to build a corpus for this task, it is necessary to obtain the (automatic or manual) transcription of each meeting, and then to segment and align it with the corresponding manual report to produce training examples suitable for training.
Following each patient visit, physicians draft long semi-structured clinical summaries called SOAP notes.
Transcripts of natural, multi-person meetings differ significantly from documents like news articles, which can make Natural Language Generation models for generating summaries unfocused.
Automatic summarization techniques on meeting conversations developed so far have been primarily extractive, resulting in poor summaries.