Data-driven enabled approaches for criteria-based video summarization: a comprehensive survey, taxonomy, and future directions

Sabha, Ambreen; Selwal, Arvind

doi:10.1007/s11042-023-14925-w

Cited by 10 publications

(1 citation statement)

References 115 publications

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“…Video summarization provides condensed content in surveillance systems, online learning platforms, and social media [3]. It aids in identifying events in traffic monitoring systems [4], serves as a resource in healthcare and education [5,6], and assists us in navigating through the immense volume of video data [7].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Video Summarization via Deep Reinforcement Learning With Shot-Level Semantics

Yuan

Zhang

2023

IEEE Trans. Circuits Syst. Video Technol.

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This paper presents a novel approach for unsupervised video summarization using reinforcement learning. It aims to address the existing limitations of current unsupervised methods, including unstable training of adversarial generatordiscriminator architectures and reliance on hand-crafted reward functions for quality evaluation. The proposed method is based on the concept that a concise and informative summary should result in a reconstructed video that closely resembles the original. The summarizer model assigns an importance score to each frame and generates a video summary. In the proposed scheme, reinforcement learning, coupled with a unique reward generation pipeline, is employed to train the summarizer model. The reward generation pipeline trains the summarizer to create summaries that lead to improved reconstructions. It comprises a generator model capable of reconstructing masked frames from a partially masked video, along with a reward mechanism that compares the reconstructed video from the summary against the original. The video generator is trained in a self-supervised manner to reconstruct randomly masked frames, enhancing its ability to generate accurate summaries. This training pipeline results in a summarizer model that better mimics human-generated video summaries compared to methods relying on hand-crafted rewards. The training process consists of two stable and isolated training steps, unlike adversarial architectures. Experimental results demonstrate promising performance, with F-scores of 62.3 and 54.5 on TVSum and SumMe datasets, respectively. Additionally, the inference stage is 300 times faster than our previously reported state-of-the-art method.

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Section: Introductionmentioning

confidence: 99%

Unsupervised Video Summarization via Deep Reinforcement Learning With Shot-Level Semantics

Yuan

Zhang

2023

IEEE Trans. Circuits Syst. Video Technol.

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A topic modeling‐based bibliometric exploration of automatic summarization research

Chen,

Xie,

Tao

et al. 2024

WIREs Data Min & Knowl

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The surge in text data has driven extensive research into developing diverse automatic summarization approaches to effectively handle vast textual information. There are several reviews on this topic, yet no large‐scale analysis based on quantitative approaches has been conducted. To provide a comprehensive overview of the field, this study conducted a bibliometric analysis of 3108 papers published from 2010 to 2022, focusing on automatic summarization research regarding topics and trends, top sources, countries/regions, institutions, researchers, and scientific collaborations. We have identified the following trends. First, the number of papers has experienced 65% growth, with the majority being published in computer science conferences. Second, Asian countries and institutions, notably China and India, actively engage in this field and demonstrate a strong inclination toward inter‐regional international collaboration, contributing to more than 24% and 20% of the output, respectively. Third, researchers show a high level of interest in multihead and attention mechanisms, graph‐based semantic analysis, and topic modeling and clustering techniques, with each topic having a prevalence of over 10%. Finally, scholars have been increasingly interested in self‐supervised and zero/few‐shot learning, multihead and attention mechanisms, and temporal analysis and event detection. This study is valuable when it comes to enhancing scholars' and practitioners' understanding of the current hotspots and future directions in automatic summarization.This article is categorized under: Algorithmic Development > Text Mining

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An End-to-End Deep Learning Approach for Video Captioning Through Mobile Devices

Pezzuto Damaceno,

Cesar

2023

Lecture Notes in Computer Science

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Data-driven enabled approaches for criteria-based video summarization: a comprehensive survey, taxonomy, and future directions

Cited by 10 publications

References 115 publications

Unsupervised Video Summarization via Deep Reinforcement Learning With Shot-Level Semantics

Unsupervised Video Summarization via Deep Reinforcement Learning With Shot-Level Semantics

A topic modeling‐based bibliometric exploration of automatic summarization research

An End-to-End Deep Learning Approach for Video Captioning Through Mobile Devices

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