# MF2Summ: Multimodal Fusion for Video Summarization with Temporal Alignment

arXiv: https://arxiv.org/abs/2506.10430 · HTML: https://mlsanity.com/paper/2506.10430/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 12 Jun 2025.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [SoundNet: Learning Sound Representations from Unlabeled Video](https://mlsanity.com/paper/1610.09001/) (2016)
- [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)
- [CLIP-It! Language-Guided Video Summarization](https://mlsanity.com/paper/2107.00650/) (2021)
- [Video Summarization Using Fully Convolutional Sequence Networks](https://mlsanity.com/paper/1805.10538/) (2018)
- [Summarizing Videos with Attention](https://mlsanity.com/paper/1812.01969/) (2018)
- [Video Summarization with Attention-Based Encoder-Decoder Networks](https://mlsanity.com/paper/1708.09545/) (2017)
- [A Survey on Multi-modal Summarization](https://mlsanity.com/paper/2109.05199/) (2021)
- [Weakly Supervised Video Summarization by Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2001.05864/) (2020)

## Built on by (most cited first, 1 of 1)

- [TR-EduVSum: A Turkish-Focused Dataset and Consensus Framework for Educational Video Summarization](https://mlsanity.com/paper/2604.07553/) (2026)

## Abstract (arXiv)

The rapid proliferation of online video content necessitates effective video summarization techniques. Traditional methods, often relying on a single modality (typically visual), struggle to capture the full semantic richness of videos. This paper introduces MF2Summ, a novel video summarization model based on multimodal content understanding, integrating both visual and auditory information. MF2Summ employs a five-stage process: feature extraction, cross-modal attention interaction, feature fusion, segment prediction, and key shot selection. Visual features are extracted using a pre-trained GoogLeNet model, while auditory features are derived using SoundNet. The core of our fusion mechanism involves a cross-modal Transformer and an alignment-guided self-attention Transformer, designed to effectively model inter-modal dependencies and temporal correspondences. Segment importance, location, and center-ness are predicted, followed by key shot selection using Non-Maximum Suppression (NMS) and the Kernel Temporal Segmentation (KTS) algorithm. Experimental results on the SumMe and TVSum datasets demonstrate that MF2Summ achieves competitive performance, notably improving F1-scores by 1.9% and 0.6% respectively over the DSNet model, and performing favorably against other state-of-the-art methods.