# Video Summarization Techniques: A Comprehensive Review

arXiv: https://arxiv.org/abs/2410.04449 · HTML: https://mlsanity.com/paper/2410.04449/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 56 references that are in the map.
- First posted on arXiv 6 Oct 2024.

## Builds on (most cited first, 25 of 25)

- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis](https://mlsanity.com/paper/1903.02874/) (2019)
- [FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding](https://mlsanity.com/paper/2004.06704/) (2020)
- [WikiHow: A Large Scale Text Summarization Dataset](https://mlsanity.com/paper/1810.09305/) (2018)
- [Cross-task weakly supervised learning from instructional videos](https://mlsanity.com/paper/1903.08225/) (2019)
- [Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward](https://mlsanity.com/paper/1801.00054/) (2017)
- [CLIP-It! Language-Guided Video Summarization](https://mlsanity.com/paper/2107.00650/) (2021)
- [Video Summarization Using Deep Neural Networks: A Survey](https://mlsanity.com/paper/2101.06072/) (2021)
- [Align and Attend: Multimodal Summarization with Dual Contrastive Losses](https://mlsanity.com/paper/2303.07284/) (2023)
- [Video Summarization with Attention-Based Encoder-Decoder Networks](https://mlsanity.com/paper/1708.09545/) (2017)
- [Multimodal Abstractive Summarization for How2 Videos](https://mlsanity.com/paper/1906.07901/) (2019)
- [Video Summarization by Learning from Unpaired Data](https://mlsanity.com/paper/1805.12174/) (2018)
- [TL;DW? Summarizing Instructional Videos with Task Relevance & Cross-Modal Saliency](https://mlsanity.com/paper/2208.06773/) (2022)
- [Exploring global diverse attention via pairwise temporal relation for video summarization](https://mlsanity.com/paper/2009.10942/) (2020)
- [Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net](https://mlsanity.com/paper/2106.10528/) (2021)
- [IntentVizor: Towards Generic Query Guided Interactive Video Summarization](https://mlsanity.com/paper/2109.14834/) (2021)
- [Title Generation for User Generated Videos](https://mlsanity.com/paper/1608.07068/) (2016)
- [Multi-Stream Dynamic Video Summarization](https://mlsanity.com/paper/1812.00108/) (2018)
- [Summarization of User-Generated Sports Video by Using Deep Action Recognition Features](https://mlsanity.com/paper/1709.08421/) (2017)
- [AudioVisual Video Summarization](https://mlsanity.com/paper/2105.07667/) (2021)
- [Classification of Important Segments in Educational Videos using Multimodal Features](https://mlsanity.com/paper/2010.13626/) (2020)
- [LTC-SUM: Lightweight Client-driven Personalized Video Summarization Framework Using 2D CNN](https://mlsanity.com/paper/2201.09049/) (2022)
- [VSCAN: An Enhanced Video Summarization using Density-based Spatial Clustering](https://mlsanity.com/paper/1405.0174/) (2014)
- [Use of Affective Visual Information for Summarization of Human-Centric Videos](https://mlsanity.com/paper/2107.03783/) (2021)
- [Scene Summarization: Clustering Scene Videos into Spatially Diverse Frames](https://mlsanity.com/paper/2311.17940/) (2023)

## Built on by (most cited first, 1 of 1)

- [Do Input-Level Defenses Transfer to Observation-Level Attacks on VideoLLMs?](https://mlsanity.com/paper/2609.08331/) (2026)

## Abstract (arXiv)

The rapid expansion of video content across a variety of industries, including social media, education, entertainment, and surveillance, has made video summarization an essential field of study. The current work is a survey that explores the various approaches and methods created for video summarizing, emphasizing both abstractive and extractive strategies. The process of extractive summarization involves the identification of key frames or segments from the source video, utilizing methods such as shot boundary recognition, and clustering. On the other hand, abstractive summarization creates new content by getting the essential content from the video, using machine learning models like deep neural networks and natural language processing, reinforcement learning, attention mechanisms, generative adversarial networks, and multi-modal learning. We also include approaches that incorporate the two methodologies, along with discussing the uses and difficulties encountered in real-world implementations. The paper also covers the datasets used to benchmark these techniques. This review attempts to provide a state-of-the-art thorough knowledge of the current state and future directions of video summarization research.