# Towards Data-Driven Automatic Video Editing

arXiv: https://arxiv.org/abs/1907.07345 · HTML: https://mlsanity.com/paper/1907.07345/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2019.

## Built on by (most cited first, 2 of 2)

- [From Long Videos to Engaging Clips: A Human-Inspired Video Editing Framework with Multimodal Narrative Understanding](https://mlsanity.com/paper/2507.02790/) (2025)
- [A Benchmark and Multi-Agent System for Instruction-driven Cinematic Video Compilation](https://mlsanity.com/paper/2604.10456/) (2026)

## Abstract (arXiv)

Automatic video editing involving at least the steps of selecting the most valuable footage from points of view of visual quality and the importance of action filmed; and cutting the footage into a brief and coherent visual story that would be interesting to watch is implemented in a purely data-driven manner. Visual semantic and aesthetic features are extracted by the ImageNet-trained convolutional neural network, and the editing controller is trained by an imitation learning algorithm. As a result, at test time the controller shows the signs of observing basic cinematography editing rules learned from the corpus of motion pictures masterpieces.