The Ajmide Topic Segmentation System for the ICASSP 2023 General Meeting Understanding and Generation Challenge
Beibei Hu, Qiang Li, Xianjun Xia · 2023
This paper describes our topic segmentation (TS) system submitted to the ICASSP2023 Signal Processing Grand Challenge - General Meeting Understanding and Generation challenge (MUG). We make three improvements to the official baseline system of the TS track. Firstly, considering that meeting transcriptions are usually long-form documents, we propose a PoNet-Svec-Transformers network to learn both sentence representations and document-level context. Secondly, we introduce a training data synthesis method that significantly increase the size of the training dataset. Finally, we leverage focal loss and adversarial training methods to improve system performance. Our best submission achieves a first-place score of 48.56/48.84 on the Eval/Test set in the TS task.