# Unsupervised Story Discovery from Continuous News Streams via Scalable Thematic Embedding

arXiv: https://arxiv.org/abs/2304.04099 · HTML: https://mlsanity.com/paper/2304.04099/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 57 references that are in the map.
- First posted on arXiv 8 Apr 2023.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models](https://mlsanity.com/paper/2108.08877/) (2021)
- [Deeper Text Understanding for IR with Contextual Neural Language Modeling](https://mlsanity.com/paper/1905.09217/) (2019)
- [Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!](https://mlsanity.com/paper/2004.14914/) (2020)
- [A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal](https://mlsanity.com/paper/2005.10070/) (2020)
- [Generating Representative Headlines for News Stories](https://mlsanity.com/paper/2001.09386/) (2020)
- [Text Classification Using Label Names Only: A Language Model Self-Training Approach](https://mlsanity.com/paper/2010.07245/) (2020)
- [TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters](https://mlsanity.com/paper/2201.06771/) (2022)
- [NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application](https://mlsanity.com/paper/2102.04887/) (2021)
- [Multilingual Clustering of Streaming News](https://mlsanity.com/paper/1809.00540/) (2018)
- [ProFairRec: Provider Fairness-aware News Recommendation](https://mlsanity.com/paper/2204.04724/) (2022)
- [Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream](https://mlsanity.com/paper/2206.04792/) (2022)
- [Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings](https://mlsanity.com/paper/2101.11059/) (2021)
- [Batch Clustering for Multilingual News Streaming](https://mlsanity.com/paper/2004.08123/) (2020)
- [Growing Story Forest Online from Massive Breaking News](https://mlsanity.com/paper/1803.00189/) (2018)
- [Unsupervised Key Event Detection from Massive Text Corpora](https://mlsanity.com/paper/2206.04153/) (2022)
- [Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation](https://mlsanity.com/paper/2211.01981/) (2022)
- [PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets Stream](https://mlsanity.com/paper/2302.05550/) (2023)
- [NewsEmbed: Modeling News through Pre-trained Document Representations](https://mlsanity.com/paper/2106.00590/) (2021)
- [Mining News Events from Comparable News Corpora: A Multi-Attribute Proximity Network Modeling Approach](https://mlsanity.com/paper/1911.06407/) (2019)
- [Simplifying Multilingual News Clustering Through Projection From a Shared Space](https://mlsanity.com/paper/2204.13418/) (2022)

## Built on by (most cited first, 5 of 5)

- [A Survey on Knowledge-Oriented Retrieval-Augmented Generation](https://mlsanity.com/paper/2503.10677/) (2025)
- [Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection](https://mlsanity.com/paper/2312.11891/) (2023)
- [Online Drift Detection with Maximum Concept Discrepancy](https://mlsanity.com/paper/2407.05375/) (2024)
- [Synergizing Unsupervised Episode Detection with LLMs for Large-Scale News Events](https://mlsanity.com/paper/2408.04873/) (2024)
- [CREAM: Continual Retrieval on Dynamic Streaming Corpora with Adaptive Soft Memory](https://mlsanity.com/paper/2601.02708/) (2026)

## Abstract (arXiv)

Unsupervised discovery of stories with correlated news articles in real-time helps people digest massive news streams without expensive human annotations. A common approach of the existing studies for unsupervised online story discovery is to represent news articles with symbolic- or graph-based embedding and incrementally cluster them into stories. Recent large language models are expected to improve the embedding further, but a straightforward adoption of the models by indiscriminately encoding all information in articles is ineffective to deal with text-rich and evolving news streams. In this work, we propose a novel thematic embedding with an off-the-shelf pretrained sentence encoder to dynamically represent articles and stories by considering their shared temporal themes. To realize the idea for unsupervised online story discovery, a scalable framework USTORY is introduced with two main techniques, theme- and time-aware dynamic embedding and novelty-aware adaptive clustering, fueled by lightweight story summaries. A thorough evaluation with real news data sets demonstrates that USTORY achieves higher story discovery performances than baselines while being robust and scalable to various streaming settings.