# Cluster-based Video Summarization with Temporal Context Awareness

arXiv: https://arxiv.org/abs/2404.04511 · HTML: https://mlsanity.com/paper/2404.04511/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 6 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Video Summarization with Long Short-term Memory](https://mlsanity.com/paper/1605.08110/) (2016)
- [Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward](https://mlsanity.com/paper/1801.00054/) (2017)
- [Video Summarization Using Deep Neural Networks: A Survey](https://mlsanity.com/paper/2101.06072/) (2021)
- [Video Summarization Using Fully Convolutional Sequence Networks](https://mlsanity.com/paper/1805.10538/) (2018)
- [Summarizing Videos with Attention](https://mlsanity.com/paper/1812.01969/) (2018)
- [Hierarchical Recurrent Neural Network for Video Summarization](https://mlsanity.com/paper/1904.12251/) (2019)
- [Discriminative Feature Learning for Unsupervised Video Summarization](https://mlsanity.com/paper/1811.09791/) (2018)
- [Exploring global diverse attention via pairwise temporal relation for video summarization](https://mlsanity.com/paper/2009.10942/) (2020)
- [VSCAN: An Enhanced Video Summarization using Density-based Spatial Clustering](https://mlsanity.com/paper/1405.0174/) (2014)

## Built on by (most cited first, 6 of 6)

- [HierSum: A Global and Local Attention Mechanism for Video Summarization](https://mlsanity.com/paper/2504.18689/) (2025)
- [TriPSS: A Tri-Modal Keyframe Extraction Framework Using Perceptual, Structural, and Semantic Representations](https://mlsanity.com/paper/2506.05395/) (2025)
- [From Captions to Keyframes: KeyScore for Multimodal Frame Scoring and Video-Language Understanding](https://mlsanity.com/paper/2510.06509/) (2025)
- [Video Dataset Condensation with Diffusion Models](https://mlsanity.com/paper/2505.06670/) (2025)
- [Leveraging Large Language Models for Information Verification -- an Engineering Approach](https://mlsanity.com/paper/2506.18274/) (2025)
- [ClimateVID -- Social Media Videos Analysis and Challenges Involved](https://mlsanity.com/paper/2604.27968/) (2026)

## Abstract (arXiv)

In this paper, we present TAC-SUM, a novel and efficient training-free approach for video summarization that addresses the limitations of existing cluster-based models by incorporating temporal context. Our method partitions the input video into temporally consecutive segments with clustering information, enabling the injection of temporal awareness into the clustering process, setting it apart from prior cluster-based summarization methods. The resulting temporal-aware clusters are then utilized to compute the final summary, using simple rules for keyframe selection and frame importance scoring. Experimental results on the SumMe dataset demonstrate the effectiveness of our proposed approach, outperforming existing unsupervised methods and achieving comparable performance to state-of-the-art supervised summarization techniques. Our source code is available for reference at urlhttps://github.com/hcmus-thesis-gulu/TAC-SUM.