THE AJMIDE TOPIC SEGMENTATION SYSTEM FOR THE ICASSP 2023 GENERAL MEETING UNDERSTANDING AND GENERATION CHALLENGE

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.