TeaserGen: Generating Teasers for Long Documentaries

• URL: http://arxiv.org/abs/2410.05586v1
• Date: Tue, 8 Oct 2024 01:00:09 GMT
• Title: TeaserGen: Generating Teasers for Long Documentaries
• Authors: Weihan Xu, Paul Pu Liang, Haven Kim, Julian McAuley, Taylor Berg-Kirkpatrick, Hao-Wen Dong,
• Abstract summary: We present DocumentaryNet, a collection of 1,269 documentaries paired with their teasers. We propose a new two-stage system for generating teasers from long documentaries.
• Score: 59.8220642722399
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Teasers are an effective tool for promoting content in entertainment, commercial and educational fields. However, creating an effective teaser for long videos is challenging for it requires long-range multimodal modeling on the input videos, while necessitating maintaining audiovisual alignments, managing scene changes and preserving factual accuracy for the output teasers. Due to the lack of a publicly-available dataset, progress along this research direction has been hindered. In this work, we present DocumentaryNet, a collection of 1,269 documentaries paired with their teasers, featuring multimodal data streams of video, speech, music, sound effects and narrations. With DocumentaryNet, we propose a new two-stage system for generating teasers from long documentaries. The proposed TeaserGen system first generates the teaser narration from the transcribed narration of the documentary using a pretrained large language model, and then selects the most relevant visual content to accompany the generated narration through language-vision models. For narration-video matching, we explore two approaches: a pretraining-based model using pretrained contrastive language-vision models and a deep sequential model that learns the mapping between the narrations and visuals. Our experimental results show that the pretraining-based approach is more effective at identifying relevant visual content than directly trained deep autoregressive models.

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