# Video Summarization Overview

arXiv: https://arxiv.org/abs/2210.11707 · HTML: https://mlsanity.com/paper/2210.11707/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 94 references that are in the map.
- First posted on arXiv 21 Oct 2022.

## Builds on (most cited first, 30 of 37)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [SpeedNet: Learning the Speediness in Videos](https://mlsanity.com/paper/2004.06130/) (2020)
- [Does Object Recognition Work for Everyone?](https://mlsanity.com/paper/1906.02659/) (2019)
- [Video Summarization with Long Short-term Memory](https://mlsanity.com/paper/1605.08110/) (2016)
- [Video Summarization Using Deep Neural Networks: A Survey](https://mlsanity.com/paper/2101.06072/) (2021)
- [QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries](https://mlsanity.com/paper/2107.09609/) (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)
- [Rethinking the Evaluation of Video Summaries](https://mlsanity.com/paper/1903.11328/) (2019)
- [Less is More: Learning Highlight Detection from Video Duration](https://mlsanity.com/paper/1903.00859/) (2019)
- [Hierarchical Recurrent Neural Network for Video Summarization](https://mlsanity.com/paper/1904.12251/) (2019)
- [Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach](https://mlsanity.com/paper/1707.04960/) (2017)
- [Video2GIF: Automatic Generation of Animated GIFs from Video](https://mlsanity.com/paper/1605.04850/) (2016)
- [Video Summarization by Learning from Unpaired Data](https://mlsanity.com/paper/1805.12174/) (2018)
- [To Click or Not To Click: Automatic Selection of Beautiful Thumbnails from Videos](https://mlsanity.com/paper/1609.01388/) (2016)
- [SumGraph: Video Summarization via Recursive Graph Modeling](https://mlsanity.com/paper/2007.08809/) (2020)
- [Query-Focused Extractive Video Summarization](https://mlsanity.com/paper/1607.05177/) (2016)
- [Pano2Vid: Automatic Cinematography for Watching 360circ Videos](https://mlsanity.com/paper/1612.02335/) (2016)
- [Query-adaptive Video Summarization via Quality-aware Relevance Estimation](https://mlsanity.com/paper/1705.00581/) (2017)
- [Adaptive Video Highlight Detection by Learning from User History](https://mlsanity.com/paper/2007.09598/) (2020)
- [PHD-GIFs: Personalized Highlight Detection for Automatic GIF Creation](https://mlsanity.com/paper/1804.06604/) (2018)
- [Collaborative Summarization of Topic-Related Videos](https://mlsanity.com/paper/1706.03114/) (2017)
- [TRECVID 2020: A comprehensive campaign for evaluating video retrieval tasks across multiple application domains](https://mlsanity.com/paper/2104.13473/) (2021)
- [Making 360circ Video Watchable in 2D: Learning Videography for Click Free Viewing](https://mlsanity.com/paper/1703.00495/) (2017)
- [Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization](https://mlsanity.com/paper/1511.07211/) (2015)
- [Video Highlight Prediction Using Audience Chat Reactions](https://mlsanity.com/paper/1707.08559/) (2017)
- [Transforming Multi-Concept Attention into Video Summarization](https://mlsanity.com/paper/2006.01410/) (2020)
- [VideoSET: Video Summary Evaluation through Text](https://mlsanity.com/paper/1406.5824/) (2014)

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

- [Lotus: Creating Short Videos From Long Videos With Abstractive and Extractive Summarization](https://mlsanity.com/paper/2502.07096/) (2025)
- [Laugh, Relate, Engage: Stylized Comment Generation for Short Videos](https://mlsanity.com/paper/2511.03757/) (2025)
- [Reinforcement Learning for Unsupervised Video Summarization with Reward Generator Training](https://mlsanity.com/paper/2407.04258/) (2024)
- [Graph Unfolding and Sampling for Transitory Video Keyframe Selection via Gershgorin Disc Alignment](https://mlsanity.com/paper/2408.01859/) (2024)
- [Does SpatioTemporal information benefit Two video summarization benchmarks?](https://mlsanity.com/paper/2410.03323/) (2024)
- [LOLGORITHM: Funny Comment Generation Agent For Short Videos](https://mlsanity.com/paper/2604.09729/) (2026)

## Abstract (arXiv)

With the broad growth of video capturing devices and applications on the web, it is more demanding to provide desired video content for users efficiently. Video summarization facilitates quickly grasping video content by creating a compact summary of videos. Much effort has been devoted to automatic video summarization, and various problem settings and approaches have been proposed. Our goal is to provide an overview of this field. This survey covers early studies as well as recent approaches which take advantage of deep learning techniques. We describe video summarization approaches and their underlying concepts. We also discuss benchmarks and evaluations. We overview how prior work addressed evaluation and detail the pros and cons of the evaluation protocols. Last but not least, we discuss open challenges in this field.