# Batch Clustering for Multilingual News Streaming

arXiv: https://arxiv.org/abs/2004.08123 · HTML: https://mlsanity.com/paper/2004.08123/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2020.

## Built on by (most cited first, 9 of 9)

- [Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings](https://mlsanity.com/paper/2101.11059/) (2021)
- [Unsupervised Key Event Detection from Massive Text Corpora](https://mlsanity.com/paper/2206.04153/) (2022)
- [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)
- [Robust Domain Adaptation for Pre-trained Multilingual Neural Machine Translation Models](https://mlsanity.com/paper/2210.14979/) (2022)
- [Russian News Clustering and Headline Selection Shared Task](https://mlsanity.com/paper/2105.00981/) (2021)
- [Simplifying Multilingual News Clustering Through Projection From a Shared Space](https://mlsanity.com/paper/2204.13418/) (2022)
- [Topic Detection and Tracking with Time-Aware Document Embeddings](https://mlsanity.com/paper/2112.06166/) (2021)
- [Contextualizing Emerging Trends in Financial News Articles](https://mlsanity.com/paper/2301.11318/) (2023)

## Abstract (arXiv)

Nowadays, digital news articles are widely available, published by various editors and often written in different languages. This large volume of diverse and unorganized information makes human reading very difficult or almost impossible. This leads to a need for algorithms able to arrange high amount of multilingual news into stories. To this purpose, we extend previous works on Topic Detection and Tracking, and propose a new system inspired from newsLens. We process articles per batch, looking for monolingual local topics which are then linked across time and languages. Here, we introduce a novel "replaying" strategy to link monolingual local topics into stories. Besides, we propose new fine tuned multilingual embedding using SBERT to create crosslingual stories. Our system gives monolingual state-of-the-art results on dataset of Spanish and German news and crosslingual state-of-the-art results on English, Spanish and German news.