# Role of Audio in Audio-Visual Video Summarization

**Authors:** Ibrahim Shoer, Berkay Kopru, Engin Erzin  
**arXiv:** [2212.01040](https://arxiv.org/abs/2212.01040)  
**Date:** 2022-12-02  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2212.01040v1/

## Key points

- Video summarization is essential for efficient video representation, retrieval, and browsing
- Most video summarization techniques rely on visual information, but recent literature highlights the advantages of audio-visual modeling
- Researchers propose a novel audio-visual video summarization framework that integrates four methods of audio-visual information fusion using GRU-based and attention-based networks
- They introduce a new explainability methodology called audio-visual canonical correlation analysis (CCA) to understand the role of audio in video summarization
- The proposed framework is evaluated using the TVSum dataset and shows improvements in F1 score and Kendall-tau score for audio-visual video summarization
- Splitting the video content based on audio-visual CCA into positively and negatively correlated videos leads to significant performance enhancements for both audio-only and audio-visual video summarization compared to positively correlated videos
- This study contributes to advancing the field of video summarization by emphasizing the importance of incorporating audio information into existing techniques
- The proposed framework and explainability methodology offer promising results for enhancing video summarization techniques in real-world applications.

## Summary

The study focuses on video summarization, which is essential for efficient video representation, retrieval, and browsing to address the challenges of volume and traffic surge. While most video summarization techniques primarily rely on visual information, recent literature highlights the advantages of audio-visual modeling. The audio channel in videos can provide valuable insights through audio-visual correlation within the content. To address this, the researchers propose a novel audio-visual video summarization framework that integrates four methods of audio-visual information fusion using GRU-based and attention-based networks. Additionally, they introduce a new explainability methodology called audio-visual canonical correlation analysis (CCA) to gain a better understanding of the role of audio in video summarization. The proposed framework is evaluated using the TVSum dataset, and it demonstrates improvements in F1 score and Kendall-tau score for audio-visual video summarization. Furthermore, by splitting the video content based on audio-visual CCA into positively and negatively correlated videos, significant performance enhancements are observed for both audio-only and audio-visual video summarization compared to positively correlated videos. Overall, this study contributes to advancing the field of video summarization by emphasizing the importance of incorporating audio information into existing techniques. The proposed framework and explainability methodology offer promising results for enhancing video summarization techniques and improving their effectiveness in real world applications.