The advent of spoken language processing (SLP) technologies on meeting transcripts is crucial for distilling, organizing, and prioritizing information. Meeting transcripts impose two key challenges to SLP tasks. First, meeting transcripts exhibit a wide variety of spoken language phenomena, leading to dramatic performance degradation. Second, meeting transcripts are usually long-form documents with several thousand words or more, posing a great challenge to mainstay Transformer-based models with high computational complexity. Publicly available meeting corpora supporting SLP tasks are quite limited and on a small scale, severely hindering the advancement of SLP on meetings. To fuel the SLP research on meetings, we launch a General Meeting Understanding and Generation (MUG) challenge. To facilitate the MUG challenge, we construct and release a meeting dataset, the Alimeeting4MUG Corpus (AMC). To the best of our knowledge, AMC is so far the largest meeting corpus in scale and facilitates the most SLP tasks. The MUG challenge includes five tracks, namely, Track 1 Topic Segmentation, Track 2 Topic-level and Session-level Extractive Summarization, Track 3 Topic Title Generation, Track 4 Keyphrase Extraction, and Track 5 Action Item Detection.
Visit the Challenge website for details and more information!
