# Supervised Video Summarization via Multiple Feature Sets with Parallel Attention

arXiv: https://arxiv.org/abs/2104.11530 · HTML: https://mlsanity.com/paper/2104.11530/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 23 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Video Summarization with Long Short-term Memory](https://mlsanity.com/paper/1605.08110/) (2016)
- [Summarizing Videos with Attention](https://mlsanity.com/paper/1812.01969/) (2018)
- [Video Summarization with Attention-Based Encoder-Decoder Networks](https://mlsanity.com/paper/1708.09545/) (2017)
- [Rethinking the Evaluation of Video Summaries](https://mlsanity.com/paper/1903.11328/) (2019)
- [Transforming Multi-Concept Attention into Video Summarization](https://mlsanity.com/paper/2006.01410/) (2020)

## Built on by (most cited first, 26 of 26)

- [V2Xum-LLM: Cross-Modal Video Summarization with Temporal Prompt Instruction Tuning](https://mlsanity.com/paper/2404.12353/) (2024)
- [OCSampler: Compressing Videos to One Clip with Single-step Sampling](https://mlsanity.com/paper/2201.04388/) (2022)
- [CSTA: CNN-based Spatiotemporal Attention for Video Summarization](https://mlsanity.com/paper/2405.11905/) (2024)
- [Progressive Video Summarization via Multimodal Self-supervised Learning](https://mlsanity.com/paper/2201.02494/) (2022)
- [Video Summarization with Large Language Models](https://mlsanity.com/paper/2504.11199/) (2025)
- [Multi-modal Segment Assemblage Network for Ad Video Editing with Importance-Coherence Reward](https://mlsanity.com/paper/2209.12164/) (2022)
- [Multimodal Frame-Scoring Transformer for Video Summarization](https://mlsanity.com/paper/2207.01814/) (2022)
- [VideoSAGE: Video Summarization with Graph Representation Learning](https://mlsanity.com/paper/2404.10539/) (2024)
- [SummDiff: Generative Modeling of Video Summarization with Diffusion](https://mlsanity.com/paper/2510.08458/) (2025)
- [Language-Guided Self-Supervised Video Summarization Using Text Semantic Matching Considering the Diversity of the Video](https://mlsanity.com/paper/2405.08890/) (2024)
- [Prompts to Summaries: Zero-Shot Language-Guided Video Summarization with Large Language and Video Models](https://mlsanity.com/paper/2506.10807/) (2025)
- [MS-Temba: Multi-Scale Temporal Mamba for Understanding Long Untrimmed Videos](https://mlsanity.com/paper/2501.06138/) (2025)
- [TRIM: A Self-Supervised Video Summarization Framework Maximizing Temporal Relative Information and Representativeness](https://mlsanity.com/paper/2506.20588/) (2025)
- [TripleSumm: Adaptive Triple-Modality Fusion for Video Summarization](https://mlsanity.com/paper/2603.01169/) (2026)
- [Learning to Summarize Videos by Contrasting Clips](https://mlsanity.com/paper/2301.05213/) (2023)
- ["Previously on ..." From Recaps to Story Summarization](https://mlsanity.com/paper/2405.11487/) (2024)
- [TRIMMER: A New Paradigm for Video Summarization through Self-Supervised Reinforcement Learning](https://mlsanity.com/paper/2605.01659/) (2026)
- [MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing](https://mlsanity.com/paper/2607.25300/) (2026)
- [Learning Subjective Time-Series Data via Utopia Label Distribution Approximation](https://mlsanity.com/paper/2307.07682/) (2023)
- [Reinforcement Learning for Unsupervised Video Summarization with Reward Generator Training](https://mlsanity.com/paper/2407.04258/) (2024)
- [Graph Unfolding and Sampling for Transitory Video Keyframe Selection via Gershgorin Disc Alignment](https://mlsanity.com/paper/2408.01859/) (2024)
- [EDSNet: Efficient-DSNet for Video Summarization](https://mlsanity.com/paper/2409.14724/) (2024)
- [Does SpatioTemporal information benefit Two video summarization benchmarks?](https://mlsanity.com/paper/2410.03323/) (2024)
- [Enhancing Video Memorability Prediction with Text-Motion Cross-modal Contrastive Loss and Its Application in Video Summarization](https://mlsanity.com/paper/2506.08649/) (2025)
- [Context-Aware Pseudo-Label Scoring for Zero-Shot Video Summarization](https://mlsanity.com/paper/2510.17501/) (2025)
- [HAS: Highlight-guided Attention Steering for Multimodal LLM Video Summarization](https://mlsanity.com/paper/2607.17994/) (2026)

## Abstract (arXiv)

The assignment of importance scores to particular frames or (short) segments in a video is crucial for summarization, but also a difficult task. Previous work utilizes only one source of visual features. In this paper, we suggest a novel model architecture that combines three feature sets for visual content and motion to predict importance scores. The proposed architecture utilizes an attention mechanism before fusing motion features and features representing the (static) visual content, i.e., derived from an image classification model. Comprehensive experimental evaluations are reported for two well-known datasets, SumMe and TVSum. In this context, we identify methodological issues on how previous work used these benchmark datasets, and present a fair evaluation scheme with appropriate data splits that can be used in future work. When using static and motion features with parallel attention mechanism, we improve state-of-the-art results for SumMe, while being on par with the state of the art for the other dataset.