# Multimodal Chaptering for Long-Form TV Newscast Video

arXiv: https://arxiv.org/abs/2406.17590 · HTML: https://mlsanity.com/paper/2406.17590/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning](https://mlsanity.com/paper/2302.14115/) (2023)
- [Multimodal Pretraining for Dense Video Captioning](https://mlsanity.com/paper/2011.11760/) (2020)
- [Learnable Optimal Sequential Grouping for Video Scene Detection](https://mlsanity.com/paper/2205.08249/) (2022)

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

- [Chapter-Llama: Efficient Chaptering in Hour-Long Videos with LLMs](https://mlsanity.com/paper/2504.00072/) (2025)

## Abstract (arXiv)

We propose a novel approach for automatic chaptering of TV newscast videos, addressing the challenge of structuring and organizing large collections of unsegmented broadcast content. Our method integrates both audio and visual cues through a two-stage process involving frozen neural networks and a trained LSTM network. The first stage extracts essential features from separate modalities, while the LSTM effectively fuses these features to generate accurate segment boundaries. Our proposed model has been evaluated on a diverse dataset comprising over 500 TV newscast videos of an average of 41 minutes gathered from TF1, a French TV channel, with varying lengths and topics. Experimental results demonstrate that this innovative fusion strategy achieves state of the art performance, yielding a high precision rate of 82% at IoU of 90%. Consequently, this approach significantly enhances analysis, indexing and storage capabilities for TV newscast archives, paving the way towards efficient management and utilization of vast audiovisual resources.