# Video Summarization using Deep Semantic Features

arXiv: https://arxiv.org/abs/1609.08758 · HTML: https://mlsanity.com/paper/1609.08758/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2016.

## Built on by (most cited first, 15 of 15)

- [Video Summarization Using Deep Neural Networks: A Survey](https://mlsanity.com/paper/2101.06072/) (2021)
- [GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization](https://mlsanity.com/paper/2104.12465/) (2021)
- [MMSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videos](https://mlsanity.com/paper/2306.04216/) (2023)
- [Causalainer: Causal Explainer for Automatic Video Summarization](https://mlsanity.com/paper/2305.00455/) (2023)
- [MHMS: Multimodal Hierarchical Multimedia Summarization](https://mlsanity.com/paper/2204.03734/) (2022)
- [Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment](https://mlsanity.com/paper/2210.04722/) (2022)
- [Conditional Modeling Based Automatic Video Summarization](https://mlsanity.com/paper/2311.12159/) (2023)
- [HierSum: A Global and Local Attention Mechanism for Video Summarization](https://mlsanity.com/paper/2504.18689/) (2025)
- [Prompts to Summaries: Zero-Shot Language-Guided Video Summarization with Large Language and Video Models](https://mlsanity.com/paper/2506.10807/) (2025)
- [MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video Summarization](https://mlsanity.com/paper/2204.08352/) (2022)
- [Converging Dimensions: Information Extraction and Summarization through Multisource, Multimodal, and Multilingual Fusion](https://mlsanity.com/paper/2406.13715/) (2024)
- [Efficient Retail Video Annotation: A Robust Key Frame Generation Approach for Product and Customer Interaction Analysis](https://mlsanity.com/paper/2506.14854/) (2025)
- [Towards an Automated Multimodal Approach for Video Summarization: Building a Bridge Between Text, Audio and Facial Cue-Based Summarization](https://mlsanity.com/paper/2506.23714/) (2025)
- [Multimodal Abstractive Summarization of Instructional Videos with Vision-Language Models](https://mlsanity.com/paper/2605.11959/) (2026)
- [Scribby: A Multi-Level LLM Framework for Semantic Video Analysis](https://mlsanity.com/paper/2606.14762/) (2026)

## Abstract (arXiv)

This paper presents a video summarization technique for an Internet video to provide a quick way to overview its content. This is a challenging problem because finding important or informative parts of the original video requires to understand its content. Furthermore the content of Internet videos is very diverse, ranging from home videos to documentaries, which makes video summarization much more tough as prior knowledge is almost not available. To tackle this problem, we propose to use deep video features that can encode various levels of content semantics, including objects, actions, and scenes, improving the efficiency of standard video summarization techniques. For this, we design a deep neural network that maps videos as well as descriptions to a common semantic space and jointly trained it with associated pairs of videos and descriptions. To generate a video summary, we extract the deep features from each segment of the original video and apply a clustering-based summarization technique to them. We evaluate our video summaries using the SumMe dataset as well as baseline approaches. The results demonstrated the advantages of incorporating our deep semantic features in a video summarization technique.