Overview of the ICASSP 2023 General Meeting Understanding and Generation Challenge (MUG)

by   Qinglin Zhang, et al.
Zhejiang University
Alibaba Group

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.


page 1

page 2


MUG: A General Meeting Understanding and Generation Benchmark

Listening to long video/audio recordings from video conferencing and onl...

Topic Modelling Meets Deep Neural Networks: A Survey

Topic modelling has been a successful technique for text analysis for al...

A Planning based Framework for Essay Generation

Generating an article automatically with computer program is a challengi...

Neural Topic Modeling of Psychotherapy Sessions

In this work, we compare different neural topic modeling methods in lear...

A New Dataset for Topic-Based Paragraph Classification in Genocide-Related Court Transcripts

Recent progress in natural language processing has been impressive in ma...

Riposte! A Large Corpus of Counter-Arguments

Constructive feedback is an effective method for improving critical thin...

Please sign up or login with your details

Forgot password? Click here to reset