# Find the Cliffhanger: Multi-Modal Trailerness in Soap Operas

arXiv: https://arxiv.org/abs/2401.16076 · HTML: https://mlsanity.com/paper/2401.16076/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 45 references that are in the map.
- First posted on arXiv 29 Jan 2024.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [MovieQA: Understanding Stories in Movies through Question-Answering](https://mlsanity.com/paper/1512.02902/) (2015)
- [MovieNet: A Holistic Dataset for Movie Understanding](https://mlsanity.com/paper/2007.10937/) (2020)
- [TransNet V2: An effective deep network architecture for fast shot transition detection](https://mlsanity.com/paper/2008.04838/) (2020)
- [Towards Long-Form Video Understanding](https://mlsanity.com/paper/2106.11310/) (2021)
- [Video Summarization with Long Short-term Memory](https://mlsanity.com/paper/1605.08110/) (2016)
- [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)
- [Hierarchical Recurrent Neural Network for Video Summarization](https://mlsanity.com/paper/1904.12251/) (2019)
- [Learning Trailer Moments in Full-Length Movies](https://mlsanity.com/paper/2008.08502/) (2020)
- [Exploring global diverse attention via pairwise temporal relation for video summarization](https://mlsanity.com/paper/2009.10942/) (2020)
- [GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization](https://mlsanity.com/paper/2104.12465/) (2021)
- [Query-controllable Video Summarization](https://mlsanity.com/paper/2004.03661/) (2020)
- [Hierarchical Multimodal Transformer to Summarize Videos](https://mlsanity.com/paper/2109.10559/) (2021)
- [Finding the Right Moment: Human-Assisted Trailer Creation via Task Composition](https://mlsanity.com/paper/2111.08774/) (2021)
- [From Trailers to Storylines: An Efficient Way to Learn from Movies](https://mlsanity.com/paper/1806.05341/) (2018)
- [Realistic Video Summarization through VISIOCITY: A New Benchmark and Evaluation Framework](https://mlsanity.com/paper/2007.14560/) (2020)

## Abstract (arXiv)

Creating a trailer requires carefully picking out and piecing together brief enticing moments out of a longer video, making it a challenging and time-consuming task. This requires selecting moments based on both visual and dialogue information. We introduce a multi-modal method for predicting the trailerness to assist editors in selecting trailer-worthy moments from long-form videos. We present results on a newly introduced soap opera dataset, demonstrating that predicting trailerness is a challenging task that benefits from multi-modal information. Code is available at https://github.com/carlobretti/cliffhanger