The field of multimodal event detection and summarization is rapidly evolving, with a focus on improving the accuracy and robustness of models in real-world environments. Recent research has explored the use of audio-visual collaboration, novel-view sound synthesis, and tri-modal fusion to enhance event detection and summarization. These innovations have shown promising results in addressing the challenges of information insufficiency, high false-positive rates, and modality deficiency. Noteworthy papers in this area include those that propose formula-supervised sound event detection, audio-visual collaboration for robust video anomaly detection, and novel-view ambient sound synthesis via visual-acoustic binding. These papers demonstrate significant improvements over existing methods and offer new insights into the limitations of current systems, providing a foundation for future improvements.