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.- Anthology ID:
- 2023.findings-eacl.96
- Volume:
- Findings of the Association for Computational Linguistics: EACL 2023
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1297–1320
- Language:
- URL:
- https://aclanthology.org/2023.findings-eacl.96
- DOI:
- Cite (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.
- Cite (Informal):
- Hierarchical3D Adapters for Long Video-to-text Summarization (Papalampidi & Lapata, Findings 2023)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2023.findings-eacl.96.pdf