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

## TL;DR

This paper explores a unified framework for multi-faceted video summarization, analyzing models that balance diversity, coverage, representation, and importance to improve summarization effectiveness across different applications.

## Contribution

It introduces a comprehensive framework for multi-faceted video summarization, emphasizing explainability and domain-specific model selection over traditional weighted model combinations.

## Key findings

- Different models suit different applications based on diversity, coverage, and importance.
- Explainability of models helps practitioners choose appropriate summarization techniques.
- Insights into feature utilization across modalities enhance summarization quality.

## Abstract

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.