Event extraction for low-resource languages such as Vietnamese is limited by the lack of large-scale annotated data. To address this, we propose a weakly supervised framework that constructs a silver corpus via pseudo-labeling. We introduce a cross-document n-ary relation filtering strategy to reduce noise by leveraging consistency across multiple articles describing the same event, and further enhance data diversity with schema-based augmentation. Experiments on the BKEE benchmark show consistent improvements, demonstrating the effectiveness of our approach. Data is available at: https://github.com/Larken1612/VietEE2.