Video Summarization Techniques: A Comprehensive Review

TL;DR

This comprehensive review surveys various video summarization techniques, including extractive and abstractive methods, discussing their approaches, challenges, datasets, and future research directions in the field.

Contribution

It provides an extensive overview of current video summarization methods, highlighting recent advances and combining insights on both extractive and abstractive strategies.

Findings

Survey of both extractive and abstractive techniques

Discussion of datasets used for benchmarking

Identification of challenges and future directions

Abstract

The rapid expansion of video content across a variety of industries, including social media, education, entertainment, and surveillance, has made video summarization an essential field of study. The current work is a survey that explores the various approaches and methods created for video summarizing, emphasizing both abstractive and extractive strategies. The process of extractive summarization involves the identification of key frames or segments from the source video, utilizing methods such as shot boundary recognition, and clustering. On the other hand, abstractive summarization creates new content by getting the essential content from the video, using machine learning models like deep neural networks and natural language processing, reinforcement learning, attention mechanisms, generative adversarial networks, and multi-modal learning. We also include approaches that incorporate the two methodologies, along with discussing the uses and difficulties encountered in real-world implementations. The paper also covers the datasets used to benchmark these techniques. This review attempts to provide a state-of-the-art thorough knowledge of the current state and future directions of video summarization research.