# Russian News Clustering and Headline Selection Shared Task

arXiv: https://arxiv.org/abs/2105.00981 · HTML: https://mlsanity.com/paper/2105.00981/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 54 references that are in the map.
- First posted on arXiv 3 May 2021.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language](https://mlsanity.com/paper/1905.07213/) (2019)
- [Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media](https://mlsanity.com/paper/1610.09786/) (2016)
- [Multilingual Clustering of Streaming News](https://mlsanity.com/paper/1809.00540/) (2018)
- [Ground Truth Bias in External Cluster Validity Indices](https://mlsanity.com/paper/1606.05596/) (2016)
- [Batch Clustering for Multilingual News Streaming](https://mlsanity.com/paper/2004.08123/) (2020)
- [Enhancing Sentence Embedding with Generalized Pooling](https://mlsanity.com/paper/1806.09828/) (2018)
- [Testing pre-trained Transformer models for Lithuanian news clustering](https://mlsanity.com/paper/2004.03461/) (2020)

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

- [Transformers for Headline Selection for Russian News Clusters](https://mlsanity.com/paper/2106.10487/) (2021)

## Abstract (arXiv)

This paper presents the results of the Russian News Clustering and Headline Selection shared task. As a part of it, we propose the tasks of Russian news event detection, headline selection, and headline generation. These tasks are accompanied by datasets and baselines. The presented datasets for event detection and headline selection are the first public Russian datasets for their tasks. The headline generation dataset is based on clustering and provides multiple reference headlines for every cluster, unlike the previous datasets. Finally, the approaches proposed by the shared task participants are reported and analyzed.