  Listening to long video/audio recordings from video conferencing and online
courses for acquiring information is extremely inefficient. Even after ASR
systems transcribe recordings into long-form spoken language documents, reading
ASR transcripts only partly speeds up seeking information. It has been observed
that a range of NLP applications, such as keyphrase extraction, topic
segmentation, and summarization, significantly improve users' efficiency in
grasping important information. The meeting scenario is among the most valuable
scenarios for deploying these spoken language processing (SLP) capabilities.
However, the lack of large-scale public meeting datasets annotated for these
SLP tasks severely hinders their advancement. To prompt SLP advancement, we
establish a large-scale general Meeting Understanding and Generation Benchmark
(MUG) to benchmark the performance of a wide range of SLP tasks, including
topic segmentation, topic-level and session-level extractive summarization and
topic title generation, keyphrase extraction, and action item detection. To
facilitate the MUG benchmark, we construct and release a large-scale meeting
dataset for comprehensive long-form SLP development, the AliMeeting4MUG Corpus,
which consists of 654 recorded Mandarin meeting sessions with diverse topic
coverage, with manual annotations for SLP tasks on manual transcripts of
meeting recordings. To the best of our knowledge, the AliMeeting4MUG Corpus is
so far the largest meeting corpus in scale and facilitates most SLP tasks. In
this paper, we provide a detailed introduction of this corpus, SLP tasks and
evaluation methods, baseline systems and their performance.
