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

Qinglin Zhang, Chong Deng, Jiaqing Liu, Hai Yu, Qian Chen +5 more
3/24/2023
cs.CLcs.SDeess.AS

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

MUG: A General Meeting Understanding and Generation Benchmark

86% match1 repo1282023

M2MeT: The ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Challenge

82% match1 repo1432021

Topic-Conversation Relevance (TCR) Dataset and Benchmarks

81% match1 repo102024

Minuteman: Machine and Human Joining Forces in Meeting Summarization

81% match2023

VCSUM: A Versatile Chinese Meeting Summarization Dataset

80% match1 repo522023

Aspect-based Meeting Transcript Summarization: A Two-Stage Approach with Weak Supervision on Sentence Classification

79% match2023

Cite this paper

@article{zhang2023overview,
  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},
  year   = {2023},
  eprint = {2303.13932},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.13932v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2303.13932.svg)](https://codeofpaper.com/paper/2303.13932)

Discussion