DeepAI AI Chat
Log In Sign Up

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

by   Vishal Kaushal, et al.
University of Massachusetts Amherst
IIT Bombay

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.


A Unified Multi-Faceted Video Summarization System

This paper addresses automatic summarization and search in visual data c...

A Formal Definition of Importance for Summarization

Research on summarization has mainly been driven by empirical approaches...

Deep Submodular Networks for Extractive Data Summarization

Deep Models are increasingly becoming prevalent in summarization problem...

Query-Specific Knowledge Summarization with Entity Evolutionary Networks

Given a query, unlike traditional IR that finds relevant documents or en...

CLIP-It! Language-Guided Video Summarization

A generic video summary is an abridged version of a video that conveys t...

A General Framework for Edited Video and Raw Video Summarization

In this paper, we build a general summarization framework for both of ed...