# AdSum: Two-stream Audio-visual Summarization for Automated Video Advertisement Clipping

arXiv: https://arxiv.org/abs/2510.26569 · HTML: https://mlsanity.com/paper/2510.26569/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Builds on (most cited first, 12 of 12)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification](https://mlsanity.com/paper/1712.04851/) (2017)
- [Seamless: Multilingual Expressive and Streaming Speech Translation](https://mlsanity.com/paper/2312.05187/) (2023)
- [TransNet V2: An effective deep network architecture for fast shot transition detection](https://mlsanity.com/paper/2008.04838/) (2020)
- [Video Summarization with Long Short-term Memory](https://mlsanity.com/paper/1605.08110/) (2016)
- [Video Summarization with Attention-Based Encoder-Decoder Networks](https://mlsanity.com/paper/1708.09545/) (2017)
- [Rethinking the Evaluation of Video Summaries](https://mlsanity.com/paper/1903.11328/) (2019)
- [CSTA: CNN-based Spatiotemporal Attention for Video Summarization](https://mlsanity.com/paper/2405.11905/) (2024)
- [Query-adaptive Video Summarization via Quality-aware Relevance Estimation](https://mlsanity.com/paper/1705.00581/) (2017)
- [Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net](https://mlsanity.com/paper/2106.10528/) (2021)
- [Scaling Up Video Summarization Pretraining with Large Language Models](https://mlsanity.com/paper/2404.03398/) (2024)
- [Summarization of User-Generated Sports Video by Using Deep Action Recognition Features](https://mlsanity.com/paper/1709.08421/) (2017)

## Abstract (arXiv)

Advertisers commonly need multiple versions of the same advertisement (ad) at varying durations for a single campaign. The traditional approach involves manually selecting and re-editing shots from longer video ads to create shorter versions, which is labor-intensive and time-consuming. In this paper, we introduce a framework for automated video ad clipping using video summarization techniques. We are the first to frame video clipping as a shot selection problem, tailored specifically for advertising. Unlike existing general video summarization methods that primarily focus on visual content, our approach emphasizes the critical role of audio in advertising. To achieve this, we develop a two-stream audio-visual fusion model that predicts the importance of video frames, where importance is defined as the likelihood of a frame being selected in the firm-produced short ad. To address the lack of ad-specific datasets, we present AdSum204, a novel dataset comprising 102 pairs of 30-second and 15-second ads from real advertising campaigns. Extensive experiments demonstrate that our model outperforms state-of-the-art methods across various metrics, including Average Precision, Area Under Curve, Spearman, and Kendall. The dataset and code are available at https://github.com/ostadabbas/AdSum204.