# Video Timeline Modeling For News Story Understanding

arXiv: https://arxiv.org/abs/2309.13446 · HTML: https://mlsanity.com/paper/2309.13446/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 65 references that are in the map.
- First posted on arXiv 23 Sep 2023.

## Builds on (most cited first, 19 of 19)

- [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)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Cross Modal Distillation for Supervision Transfer](https://mlsanity.com/paper/1507.00448/) (2015)
- [Video Representation Learning by Dense Predictive Coding](https://mlsanity.com/paper/1909.04656/) (2019)
- [Video Summarization with Long Short-term Memory](https://mlsanity.com/paper/1605.08110/) (2016)
- [Video Summarization Using Deep Neural Networks: A Survey](https://mlsanity.com/paper/2101.06072/) (2021)
- [Evolving Losses for Unsupervised Video Representation Learning](https://mlsanity.com/paper/2002.12177/) (2020)
- [The Abduction of Sherlock Holmes: A Dataset for Visual Abductive Reasoning](https://mlsanity.com/paper/2202.04800/) (2022)
- [Visual Abductive Reasoning](https://mlsanity.com/paper/2203.14040/) (2022)
- [Examining the State-of-the-Art in News Timeline Summarization](https://mlsanity.com/paper/2005.10107/) (2020)
- [FIVR: Fine-grained Incident Video Retrieval](https://mlsanity.com/paper/1809.04094/) (2018)
- [A Temporally Sensitive Submodularity Framework for Timeline Summarization](https://mlsanity.com/paper/1810.07949/) (2018)
- [How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization](https://mlsanity.com/paper/1807.04219/) (2018)
- [Diversity-aware Multi-Video Summarization](https://mlsanity.com/paper/1706.03123/) (2017)
- [NewsEmbed: Modeling News through Pre-trained Document Representations](https://mlsanity.com/paper/2106.00590/) (2021)
- [Analyzing Evolving Stories in News Articles](https://mlsanity.com/paper/1703.08593/) (2017)

## Built on by (most cited first, 3 of 3)

- [AViLA: Asynchronous Vision-Language Agent for Streaming Multimodal Data Interaction](https://mlsanity.com/paper/2506.18472/) (2025)
- [Self-Paced and Self-Corrective Masked Prediction for Movie Trailer Generation](https://mlsanity.com/paper/2512.04426/) (2025)
- [VCoME: Verbal Video Composition with Multimodal Editing Effects](https://mlsanity.com/paper/2407.04697/) (2024)

## Abstract (arXiv)

In this paper, we present a novel problem, namely video timeline modeling. Our objective is to create a video-associated timeline from a set of videos related to a specific topic, thereby facilitating the content and structure understanding of the story being told. This problem has significant potential in various real-world applications, for instance, news story summarization. To bootstrap research in this area, we curate a realistic benchmark dataset, YouTube-News-Timeline, consisting of over 12k timelines and 300k YouTube news videos. Additionally, we propose a set of quantitative metrics to comprehensively evaluate and compare methodologies. With such a testbed, we further develop and benchmark several deep learning approaches to tackling this problem. We anticipate that this exploratory work will pave the way for further research in video timeline modeling. The assets are available via https://github.com/google-research/google-research/tree/master/videotimelinemodeling.