# Summary Transfer: Exemplar-based Subset Selection for Video Summarization

arXiv: https://arxiv.org/abs/1603.03369 · HTML: https://mlsanity.com/paper/1603.03369/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2016.

## Built on by (most cited first, 24 of 24)

- [EgoVLPv2: Egocentric Video-Language Pre-training with Fusion in the Backbone](https://mlsanity.com/paper/2307.05463/) (2023)
- [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)
- [TL;DW? Summarizing Instructional Videos with Task Relevance & Cross-Modal Saliency](https://mlsanity.com/paper/2208.06773/) (2022)
- [Reconstructive Sequence-Graph Network for Video Summarization](https://mlsanity.com/paper/2105.04066/) (2021)
- [GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization](https://mlsanity.com/paper/2104.12465/) (2021)
- [Hierarchical Multimodal Transformer to Summarize Videos](https://mlsanity.com/paper/2109.10559/) (2021)
- [Enhancing Interactive Image Retrieval With Query Rewriting Using Large Language Models and Vision Language Models](https://mlsanity.com/paper/2404.18746/) (2024)
- [Towards Automated Movie Trailer Generation](https://mlsanity.com/paper/2404.03477/) (2024)
- [How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization](https://mlsanity.com/paper/2101.10514/) (2021)
- [PR-Net: Preference Reasoning for Personalized Video Highlight Detection](https://mlsanity.com/paper/2109.01799/) (2021)
- [Unsupervised Video Highlight Detection by Learning from Audio and Visual Recurrence](https://mlsanity.com/paper/2407.13933/) (2024)
- [AudioVisual Video Summarization](https://mlsanity.com/paper/2105.07667/) (2021)
- [Causal Video Summarizer for Video Exploration](https://mlsanity.com/paper/2307.01947/) (2023)
- [CFSum: A Transformer-Based Multi-Modal Video Summarization Framework With Coarse-Fine Fusion](https://mlsanity.com/paper/2503.00364/) (2025)
- [Language-Guided Graph Representation Learning for Video Summarization](https://mlsanity.com/paper/2511.10953/) (2025)
- [MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video Summarization](https://mlsanity.com/paper/2204.08352/) (2022)
- [A Multi-stage deep architecture for summary generation of soccer videos](https://mlsanity.com/paper/2205.00694/) (2022)
- ["Previously on ..." From Recaps to Story Summarization](https://mlsanity.com/paper/2405.11487/) (2024)
- [Switch-a-View: View Selection Learned from Unlabeled In-the-wild Videos](https://mlsanity.com/paper/2412.18386/) (2024)
- [MS4UI: A Dataset for Multi-modal Summarization of User Interface Instructional Videos](https://mlsanity.com/paper/2506.12623/) (2025)
- [From Captions to Keyframes: KeyScore for Multimodal Frame Scoring and Video-Language Understanding](https://mlsanity.com/paper/2510.06509/) (2025)
- [Language-guided Recursive Spatiotemporal Graph Modeling for Video Summarization](https://mlsanity.com/paper/2509.05604/) (2025)
- [Color When It Counts: Grayscale-Guided Online Triggering for Always-On Streaming Video Sensing](https://mlsanity.com/paper/2603.22466/) (2026)

## Abstract (arXiv)

Video summarization has unprecedented importance to help us digest, browse, and search today's ever-growing video collections. We propose a novel subset selection technique that leverages supervision in the form of human-created summaries to perform automatic keyframe-based video summarization. The main idea is to nonparametrically transfer summary structures from annotated videos to unseen test videos. We show how to extend our method to exploit semantic side information about the video's category/genre to guide the transfer process by those training videos semantically consistent with the test input. We also show how to generalize our method to subshot-based summarization, which not only reduces computational costs but also provides more flexible ways of defining visual similarity across subshots spanning several frames. We conduct extensive evaluation on several benchmarks and demonstrate promising results, outperforming existing methods in several settings.