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

arXiv: https://arxiv.org/abs/2303.13932 · HTML: https://mlsanity.com/paper/2303.13932/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 24 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding](https://mlsanity.com/paper/1908.04577/) (2019)
- [PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation](https://mlsanity.com/paper/2004.07159/) (2020)
- [PoNet: Pooling Network for Efficient Token Mixing in Long Sequences](https://mlsanity.com/paper/2110.02442/) (2021)
- [W2KPE: Keyphrase Extraction with Word-Word Relation](https://mlsanity.com/paper/2303.13463/) (2023)

## Abstract (arXiv)

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.