# A General Framework for Edited Video and Raw Video Summarization

arXiv: https://arxiv.org/abs/1904.10669 · HTML: https://mlsanity.com/paper/1904.10669/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2019.

## Built on by (most cited first, 12 of 12)

- [Video Summarization Using Deep Neural Networks: A Survey](https://mlsanity.com/paper/2101.06072/) (2021)
- [Reconstructive Sequence-Graph Network for Video Summarization](https://mlsanity.com/paper/2105.04066/) (2021)
- [Hierarchical Multimodal Transformer to Summarize Videos](https://mlsanity.com/paper/2109.10559/) (2021)
- [Video Joint Modelling Based on Hierarchical Transformer for Co-summarization](https://mlsanity.com/paper/2112.13478/) (2021)
- [An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs](https://mlsanity.com/paper/2105.01342/) (2021)
- [SEM-POS: Grammatically and Semantically Correct Video Captioning](https://mlsanity.com/paper/2303.14829/) (2023)
- [AudioVisual Video Summarization](https://mlsanity.com/paper/2105.07667/) (2021)
- [Spiking Variational Graph Representation Inference for Video Summarization](https://mlsanity.com/paper/2508.15389/) (2025)
- [Language-Guided Graph Representation Learning for Video Summarization](https://mlsanity.com/paper/2511.10953/) (2025)
- [A Multi-stage deep architecture for summary generation of soccer videos](https://mlsanity.com/paper/2205.00694/) (2022)
- [Graph Unfolding and Sampling for Transitory Video Keyframe Selection via Gershgorin Disc Alignment](https://mlsanity.com/paper/2408.01859/) (2024)
- [FullTransNet: Full Transformer with Local-Global Attention for Video Summarization](https://mlsanity.com/paper/2501.00882/) (2025)

## Abstract (arXiv)

In this paper, we build a general summarization framework for both of edited video and raw video summarization. Overall, our work can be divided into three folds: 1) Four models are designed to capture the properties of video summaries, i.e., containing important people and objects (importance), representative to the video content (representativeness), no similar key-shots (diversity) and smoothness of the storyline (storyness). Specifically, these models are applicable to both edited videos and raw videos. 2) A comprehensive score function is built with the weighted combination of the aforementioned four models. Note that the weights of the four models in the score function, denoted as property-weight, are learned in a supervised manner. Besides, the property-weights are learned for edited videos and raw videos, respectively. 3) The training set is constructed with both edited videos and raw videos in order to make up the lack of training data. Particularly, each training video is equipped with a pair of mixing-coefficients which can reduce the structure mess in the training set caused by the rough mixture. We test our framework on three datasets, including edited videos, short raw videos and long raw videos. Experimental results have verified the effectiveness of the proposed framework.