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Unsupervised Transcript-assisted Video Summarization and Highlight Detection

Spyros Barbakos, Charalampos Antoniadis, Gerasimos Potamianos, Gianluca Setti

TL;DR

This paper tackles the challenge of producing concise video summaries and detecting highlights without labeled data. It presents a multimodal reinforcement learning framework that fuses visual frames and transcripts through a Transformer with an Align & Attend mechanism, guided by a composite reward that combines diversity, representativeness, and transcript saliency. By leveraging unsupervised training on large-scale data (e.g., HowTo100M) and integrating transcript-aware signals via AREDSUM, the approach demonstrates improved ranking-based summarization and highlight localization compared to vision-only baselines. The work highlights the value of cross-modal information for summarization tasks and outlines pathways to scale with domain-specific data and additional modalities such as captions and audio.

Abstract

Video consumption is a key part of daily life, but watching entire videos can be tedious. To address this, researchers have explored video summarization and highlight detection to identify key video segments. While some works combine video frames and transcripts, and others tackle video summarization and highlight detection using Reinforcement Learning (RL), no existing work, to the best of our knowledge, integrates both modalities within an RL framework. In this paper, we propose a multimodal pipeline that leverages video frames and their corresponding transcripts to generate a more condensed version of the video and detect highlights using a modality fusion mechanism. The pipeline is trained within an RL framework, which rewards the model for generating diverse and representative summaries while ensuring the inclusion of video segments with meaningful transcript content. The unsupervised nature of the training allows for learning from large-scale unannotated datasets, overcoming the challenge posed by the limited size of existing annotated datasets. Our experiments show that using the transcript in video summarization and highlight detection achieves superior results compared to relying solely on the visual content of the video.

Unsupervised Transcript-assisted Video Summarization and Highlight Detection

TL;DR

This paper tackles the challenge of producing concise video summaries and detecting highlights without labeled data. It presents a multimodal reinforcement learning framework that fuses visual frames and transcripts through a Transformer with an Align & Attend mechanism, guided by a composite reward that combines diversity, representativeness, and transcript saliency. By leveraging unsupervised training on large-scale data (e.g., HowTo100M) and integrating transcript-aware signals via AREDSUM, the approach demonstrates improved ranking-based summarization and highlight localization compared to vision-only baselines. The work highlights the value of cross-modal information for summarization tasks and outlines pathways to scale with domain-specific data and additional modalities such as captions and audio.

Abstract

Video consumption is a key part of daily life, but watching entire videos can be tedious. To address this, researchers have explored video summarization and highlight detection to identify key video segments. While some works combine video frames and transcripts, and others tackle video summarization and highlight detection using Reinforcement Learning (RL), no existing work, to the best of our knowledge, integrates both modalities within an RL framework. In this paper, we propose a multimodal pipeline that leverages video frames and their corresponding transcripts to generate a more condensed version of the video and detect highlights using a modality fusion mechanism. The pipeline is trained within an RL framework, which rewards the model for generating diverse and representative summaries while ensuring the inclusion of video segments with meaningful transcript content. The unsupervised nature of the training allows for learning from large-scale unannotated datasets, overcoming the challenge posed by the limited size of existing annotated datasets. Our experiments show that using the transcript in video summarization and highlight detection achieves superior results compared to relying solely on the visual content of the video.

Paper Structure

This paper contains 17 sections, 7 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Proposed Approach
  3. Overview
  4. Reward Functions
  5. Diversity Reward ($R_{div}$)
  6. Representativeness Reward ($R_{rep}$)
  7. Text Saliency Reward ($R_{sal}$)
  8. Multimodal Transformer
  9. Training with Policy Gradient
  10. Experiments and Results
  11. Setup
  12. Datasets
  13. Evaluation Metrics
  14. Video Summarization Evaluation
  15. Highlight Detection Evaluation
  16. ...and 2 more sections

Figures (2)

  • Figure 1: Block diagram of the proposed RL-based deep-learning solution to video summarization and highlight detection. The snowflakes indicate that the models are only used for inference.
  • Figure 2: The internal structure of the multimodal Transformer in our solution. See the Align & Attend module that illustrates how the video and text modalities are aligned.