Overview of the ICASSP 2023 General Meeting Understanding and Generation Challenge (MUG)
Qinglin Zhang, Chong Deng, Jiaqing Liu, Hai Yu, Qian Chen, Wen Wang, Zhijie Yan, Jinglin Liu
Abstract
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. We review the dataset, track settings and baselines, and summarize the challenge results and major techniques used in the submissions.
BibTeX
@inproceedings{icassp2023_overviewoftheica,
title = {Overview of the ICASSP 2023 General Meeting Understanding and Generation Challenge (MUG)},
author = {Qinglin Zhang and Chong Deng and Jiaqing Liu and Hai Yu and Qian Chen and Wen Wang and Zhijie Yan and Jinglin Liu and Yi Ren and Zhou Zhao},
booktitle = {ICASSP 2023},
year = {2023}
}