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    <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., 2022), 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.</abstract>
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%0 Conference Proceedings
%T Hierarchical3D Adapters for Long Video-to-text Summarization
%A Papalampidi, Pinelopi
%A Lapata, Mirella
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F papalampidi-lapata-2023-hierarchical3d
%X 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., 2022), 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.
%R 10.18653/v1/2023.findings-eacl.96
%U https://aclanthology.org/2023.findings-eacl.96
%U https://doi.org/10.18653/v1/2023.findings-eacl.96
%P 1297-1320

Markdown (Informal)

[Hierarchical3D Adapters for Long Video-to-text Summarization](https://aclanthology.org/2023.findings-eacl.96) (Papalampidi & Lapata, Findings 2023)

• Hierarchical3D Adapters for Long Video-to-text Summarization (Papalampidi & Lapata, Findings 2023)

ACL

• Pinelopi Papalampidi and Mirella Lapata. 2023. Hierarchical3D Adapters for Long Video-to-text Summarization. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1297–1320, Dubrovnik, Croatia. Association for Computational Linguistics.