SD-MVSum: Script-Driven Multimodal Video Summarization Method and Datasets
This work addresses video summarization for users needing concise summaries based on scripts, but it is incremental as it builds on existing methods and datasets.
The authors tackled script-driven video summarization by extending a method to incorporate both visual and spoken content relevance to user-provided scripts, achieving competitive results against state-of-the-art approaches.
In this work, we extend a recent method for script-driven video summarization, originally considering just the visual content of the video, to take into account the relevance of the user-provided script also with the video's spoken content. In the proposed method, SD-MVSum, the dependence between each considered pair of data modalities, i.e., script-video and script-transcript, is modeled using a new weighted cross-modal attention mechanism. This explicitly exploits the semantic similarity between the paired modalities in order to promote the parts of the full-length video with the highest relevance to the user-provided script. Furthermore, we extend two large-scale datasets for video summarization (S-VideoXum, MrHiSum), to make them suitable for training and evaluation of script-driven multimodal video summarization methods. Experimental comparisons document the competitiveness of our SD-MVSum method against other SOTA approaches for script-driven and generic video summarization. Our new method and extended datasets are available at: https://github.com/IDT-ITI/SD-MVSum.