# Growing Story Forest Online from Massive Breaking News

arXiv: https://arxiv.org/abs/1803.00189 · HTML: https://mlsanity.com/paper/1803.00189/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2018.

## Built on by (most cited first, 7 of 7)

- [A Survey on Event-based News Narrative Extraction](https://mlsanity.com/paper/2302.08351/) (2023)
- [Mixed Multi-Model Semantic Interaction for Graph-based Narrative Visualizations](https://mlsanity.com/paper/2302.06452/) (2023)
- [Unsupervised Story Discovery from Continuous News Streams via Scalable Thematic Embedding](https://mlsanity.com/paper/2304.04099/) (2023)
- [SCStory: Self-supervised and Continual Online Story Discovery](https://mlsanity.com/paper/2312.03725/) (2023)
- [Interactive Narrative Analytics: Bridging Computational Narrative Extraction and Human Sensemaking](https://mlsanity.com/paper/2601.11459/) (2026)
- [Background Summarization of Event Timelines](https://mlsanity.com/paper/2310.16197/) (2023)
- [An Information-Geometric Justification for Composite Coherence in Event-Based Narrative Extraction](https://mlsanity.com/paper/2606.29118/) (2026)

## Abstract (arXiv)

We describe our experience of implementing a news content organization system at Tencent that discovers events from vast streams of breaking news and evolves news story structures in an online fashion. Our real-world system has distinct requirements in contrast to previous studies on topic detection and tracking (TDT) and event timeline or graph generation, in that we 1) need to accurately and quickly extract distinguishable events from massive streams of long text documents that cover diverse topics and contain highly redundant information, and 2) must develop the structures of event stories in an online manner, without repeatedly restructuring previously formed stories, in order to guarantee a consistent user viewing experience. In solving these challenges, we propose Story Forest, a set of online schemes that automatically clusters streaming documents into events, while connecting related events in growing trees to tell evolving stories. We conducted extensive evaluation based on 60 GB of real-world Chinese news data, although our ideas are not language-dependent and can easily be extended to other languages, through detailed pilot user experience studies. The results demonstrate the superior capability of Story Forest to accurately identify events and organize news text into a logical structure that is appealing to human readers, compared to multiple existing algorithm frameworks.