Hierarchical3D Adapters for Long Video-to-text Summarization

• URL: http://arxiv.org/abs/2210.04829v1
• Date: Mon, 10 Oct 2022 16:44:36 GMT
• Title: Hierarchical3D Adapters for Long Video-to-text Summarization
• Authors: Pinelopi Papalampidi, Mirella Lapata
• Abstract summary: multimodal information offers superior performance over more memory-heavy and fully fine-tuned textual summarization methods. Our experiments demonstrate that multimodal information offers superior performance over more memory-heavy and fully fine-tuned textual summarization methods.
• Score: 79.01926022762093
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we focus on video-to-text summarization and investigate how to best utilize multimodal information for summarizing long inputs (e.g., an hour-long TV show) into long outputs (e.g., a multi-sentence summary). We extend SummScreen (Chen et al., 2021), a dialogue summarization dataset consisting of transcripts of TV episodes with reference summaries, and create a multimodal variant by collecting corresponding full-length videos. We incorporate multimodal information into a pre-trained textual summarizer efficiently using adapter modules augmented with a hierarchical structure while tuning only 3.8\% of model parameters. Our experiments demonstrate that multimodal information offers superior performance over more memory-heavy and fully fine-tuned textual summarization methods.

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