# Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity,Representation, Coverage and Importance

arXiv: https://arxiv.org/abs/1901.01153 · HTML: https://mlsanity.com/paper/1901.01153/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2019.

## Built on by (most cited first, 14 of 14)

- [PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Subset Selection](https://mlsanity.com/paper/2103.00128/) (2021)
- [STEPs: Self-Supervised Key Step Extraction and Localization from Unlabeled Procedural Videos](https://mlsanity.com/paper/2301.00794/) (2023)
- [Submodlib: A Submodular Optimization Library](https://mlsanity.com/paper/2202.10680/) (2022)
- [SMILe: Leveraging Submodular Mutual Information For Robust Few-Shot Object Detection](https://mlsanity.com/paper/2407.02665/) (2024)
- [SCoRe: Submodular Combinatorial Representation Learning](https://mlsanity.com/paper/2310.00165/) (2023)
- [How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization](https://mlsanity.com/paper/2101.10514/) (2021)
- [Looking Beyond the Known: Towards a Data Discovery Guided Open-World Object Detection](https://mlsanity.com/paper/2510.00303/) (2025)
- [FAIR: Filtering of Automatically Induced Rules](https://mlsanity.com/paper/2402.15472/) (2024)
- [STONE: A Submodular Optimization Framework for Active 3D Object Detection](https://mlsanity.com/paper/2410.03918/) (2024)
- [InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity](https://mlsanity.com/paper/2508.21003/) (2025)
- [SHaSaM: Submodular Hard Sample Mining for Fair Facial Attribute Recognition](https://mlsanity.com/paper/2602.05162/) (2026)
- [SMA: Submodular Modality Aligner For Data Efficient Multimodal Learning](https://mlsanity.com/paper/2605.12872/) (2026)
- [Improving Video Question Answering through query-based frame selection](https://mlsanity.com/paper/2601.07459/) (2026)
- [Understanding Submodular Information Measure Based Objectives for Representation Learning: A Variance and Separation Perspective](https://mlsanity.com/paper/2607.27660/) (2026)

## Abstract (arXiv)

This paper addresses automatic summarization of videos in a unified manner. In particular, we propose a framework for multi-faceted summarization for extractive, query base and entity summarization (summarization at the level of entities like objects, scenes, humans and faces in the video). We investigate several summarization models which capture notions of diversity, coverage, representation and importance, and argue the utility of these different models depending on the application. While most of the prior work on submodular summarization approaches has focused oncombining several models and learning weighted mixtures, we focus on the explainability of different models and featurizations, and how they apply to different domains. We also provide implementation details on summarization systems and the different modalities involved. We hope that the study from this paper will give insights into practitioners to appropriately choose the right summarization models for the problems at hand.