# Towards an Automated Multimodal Approach for Video Summarization: Building a Bridge Between Text, Audio and Facial Cue-Based Summarization

arXiv: https://arxiv.org/abs/2506.23714 · HTML: https://mlsanity.com/paper/2506.23714/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 14 of 14)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Video Summarization with Long Short-term Memory](https://mlsanity.com/paper/1605.08110/) (2016)
- [Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward](https://mlsanity.com/paper/1801.00054/) (2017)
- [Video Summarization Using Deep Neural Networks: A Survey](https://mlsanity.com/paper/2101.06072/) (2021)
- [Video Summarization Using Fully Convolutional Sequence Networks](https://mlsanity.com/paper/1805.10538/) (2018)
- [TL;DW? Summarizing Instructional Videos with Task Relevance & Cross-Modal Saliency](https://mlsanity.com/paper/2208.06773/) (2022)
- [Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net](https://mlsanity.com/paper/2106.10528/) (2021)
- [Hierarchical Multimodal Transformer to Summarize Videos](https://mlsanity.com/paper/2109.10559/) (2021)
- [Scaling Up Video Summarization Pretraining with Large Language Models](https://mlsanity.com/paper/2404.03398/) (2024)
- [Video Summarization using Deep Semantic Features](https://mlsanity.com/paper/1609.08758/) (2016)
- [AudioVisual Video Summarization](https://mlsanity.com/paper/2105.07667/) (2021)
- [Multimodal Frame-Scoring Transformer for Video Summarization](https://mlsanity.com/paper/2207.01814/) (2022)

## Abstract (arXiv)

The increasing volume of video content in educational, professional, and social domains necessitates effective summarization techniques that go beyond traditional unimodal approaches. This paper proposes a behaviour-aware multimodal video summarization framework that integrates textual, audio, and visual cues to generate timestamp-aligned summaries. By extracting prosodic features, textual cues and visual indicators, the framework identifies semantically and emotionally important moments. A key contribution is the identification of bonus words, which are terms emphasized across multiple modalities and used to improve the semantic relevance and expressive clarity of the summaries. The approach is evaluated against pseudo-ground truth (pGT) summaries generated using LLM-based extractive method. Experimental results demonstrate significant improvements over traditional extractive method, such as the Edmundson method, in both text and video-based evaluation metrics. Text-based metrics show ROUGE-1 increasing from 0.4769 to 0.7929 and BERTScore from 0.9152 to 0.9536, while in video-based evaluation, our proposed framework improves F1-Score by almost 23%. The findings underscore the potential of multimodal integration in producing comprehensive and behaviourally informed video summaries.