# VideoSET: Video Summary Evaluation through Text

arXiv: https://arxiv.org/abs/1406.5824 · HTML: https://mlsanity.com/paper/1406.5824/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2014.

## Built on by (most cited first, 8 of 8)

- [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)
- [DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video Summarization](https://mlsanity.com/paper/2105.06441/) (2021)
- [How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization](https://mlsanity.com/paper/2101.10514/) (2021)
- [Video Summarization Overview](https://mlsanity.com/paper/2210.11707/) (2022)
- [SD-VSum: A Method and Dataset for Script-Driven Video Summarization](https://mlsanity.com/paper/2505.03319/) (2025)
- [Language-guided Recursive Spatiotemporal Graph Modeling for Video Summarization](https://mlsanity.com/paper/2509.05604/) (2025)
- [SD-MVSum: Script-Driven Multimodal Video Summarization Method and Datasets](https://mlsanity.com/paper/2510.05652/) (2025)

## Abstract (arXiv)

In this paper we present VideoSET, a method for Video Summary Evaluation through Text that can evaluate how well a video summary is able to retain the semantic information contained in its original video. We observe that semantics is most easily expressed in words, and develop a text-based approach for the evaluation. Given a video summary, a text representation of the video summary is first generated, and an NLP-based metric is then used to measure its semantic distance to ground-truth text summaries written by humans. We show that our technique has higher agreement with human judgment than pixel-based distance metrics. We also release text annotations and ground-truth text summaries for a number of publicly available video datasets, for use by the computer vision community.