# Unsupervised Video Summarization via Multi-source Features

**Authors:** Hussain Kanafani, Junaid Ahmed Ghauri, Sherzod Hakimov, Ralph Ewerth  
**arXiv:** [2105.12532](https://arxiv.org/abs/2105.12532)  
**Date:** 2021-05-26  
**Summary page:** https://www.summarizepaper.com/en/paper/2105.12532v1/

## Key points

- The paper proposes a novel approach to video summarization that incorporates multiple feature sources with chunk and stride fusion
- Video summarization aims to generate a concise yet informative summary of the original video without requiring human annotations
- Unsupervised approaches are advantageous as they do not require such annotations and can generalize to a wider range of domains
- Previous work in this area has relied on deep features based on models pre-trained on ImageNet data, but the authors argue that incorporating multiple feature sources can provide more information about the visual content and improve summarization performance
- The proposed method achieves state-of-the-art results on both TVSum and SumMe datasets while highlighting shortcomings in previous work regarding evaluation methodology
- Error analysis is performed on videos from both benchmarks to identify factors leading to misclassifications.

## Summary

In their paper titled "Unsupervised Video Summarization via Multi-source Features," authors Hussain Kanafani, Junaid Ahmed Ghauri, Sherzod Hakimov, and Ralph Ewerth propose a novel approach to video summarization that incorporates multiple feature sources with chunk and stride fusion. The goal of video summarization is to generate a concise yet informative summary that captures the essence of the original video without requiring human annotations. Unsupervised approaches are advantageous as they do not require such annotations and can generalize to a wider range of domains. Previous work in this area has relied on deep features based on models pre-trained on ImageNet data. However, the authors argue that incorporating multiple feature sources can provide more information about the visual content and improve summarization performance. To evaluate their method, the authors compare it with four state-of-the-art approaches using two benchmark datasets: TVSum and SumMe. They also implement two of these approaches themselves to reproduce reported results. The evaluation shows that their proposed method achieves state-of-the-art results on both datasets while highlighting shortcomings in previous work regarding evaluation methodology. Additionally, the authors perform error analysis on videos from both benchmarks to identify factors leading to misclassifications. Overall, this paper presents a promising approach to unsupervised video summarization that incorporates multiple feature sources for improved performance. The evaluation results suggest that this approach outperforms existing methods and highlights areas for future research in this field.