VATMAN: Video-Audio-Text Multimodal Abstractive Summarization with Trimodal Hierarchical Multi-head Attention

Multimodal Abstractive Summarization is a challenging task that aims to generate concise and informative summaries from diverse modalities, such as video, audio, and text. In this study, we propose VATMAN, a novel approach for multimodal abstractive summarization. To effectively capture the hierarchical relationships and dependencies between modalities, we introduce Trimodal Hierarchical Multi-head Attention (THMA). THMA hierarchically attends to the video, audio, and textual representations, enabling the model to distill salient information and generate cohesive and coherent summaries. VATMAN leverages state-of-the-art generative pretrained language models (GPLMs), specifically Transformer-based models, and applies hierarchical attention at the modality level, which enhances the utilization of contextual information. The proposed VATMAN model on the How2 dataset demonstrates the ability to create more fluent summaries than those generated by human authors, showcasing its potential for utilization in various industrial environments.