  ICASSP2023 General Meeting Understanding and Generation Challenge (MUG)
focuses on prompting a wide range of spoken language processing (SLP) research
on meeting transcripts, as SLP applications are critical to improve users'
efficiency in grasping important information in meetings. MUG includes five
tracks, including topic segmentation, topic-level and session-level extractive
summarization, topic title generation, keyphrase extraction, and action item
detection. To facilitate MUG, we construct and release a large-scale meeting
dataset, the AliMeeting4MUG Corpus.
