SD-VSum: A Method and Dataset for Script-Driven Video Summarization

• URL: http://arxiv.org/abs/2505.03319v1
• Date: Tue, 06 May 2025 08:47:14 GMT
• Title: SD-VSum: A Method and Dataset for Script-Driven Video Summarization
• Authors: Manolis Mylonas, Evlampios Apostolidis, Vasileios Mezaris,
• Abstract summary: We introduce the task of script-driven video summarization (VideoXum)<n>We produce natural language descriptions of the different human-annotated summaries that are available per video.<n>We develop a new network architecture for script-driven video summarization (SD-VSum)
• Score: 6.076406622352117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we introduce the task of script-driven video summarization, which aims to produce a summary of the full-length video by selecting the parts that are most relevant to a user-provided script outlining the visual content of the desired summary. Following, we extend a recently-introduced large-scale dataset for generic video summarization (VideoXum) by producing natural language descriptions of the different human-annotated summaries that are available per video. In this way we make it compatible with the introduced task, since the available triplets of ``video, summary and summary description'' can be used for training a method that is able to produce different summaries for a given video, driven by the provided script about the content of each summary. Finally, we develop a new network architecture for script-driven video summarization (SD-VSum), that relies on the use of a cross-modal attention mechanism for aligning and fusing information from the visual and text modalities. Our experimental evaluations demonstrate the advanced performance of SD-VSum against state-of-the-art approaches for query-driven and generic (unimodal and multimodal) summarization from the literature, and document its capacity to produce video summaries that are adapted to each user's needs about their content.

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