# Dataset for Automatic Summarization of Russian News

arXiv: https://arxiv.org/abs/2006.11063 · HTML: https://mlsanity.com/paper/2006.11063/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2020.

## Built on by (most cited first, 12 of 12)

- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russian](https://mlsanity.com/paper/2206.01583/) (2022)
- [Fine-tuning GPT-3 for Russian Text Summarization](https://mlsanity.com/paper/2108.03502/) (2021)
- [DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media](https://mlsanity.com/paper/2105.10878/) (2021)
- [Facilitating large language model Russian adaptation with Learned Embedding Propagation](https://mlsanity.com/paper/2412.21140/) (2024)
- [MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs](https://mlsanity.com/paper/2505.19800/) (2025)
- [From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization](https://mlsanity.com/paper/2404.03555/) (2024)
- [DIALOG-22 RuATD Generated Text Detection](https://mlsanity.com/paper/2206.08029/) (2022)
- [The State and Fate of Summarization Datasets: A Survey](https://mlsanity.com/paper/2411.04585/) (2024)
- [WikiOmnia: generative QA corpus on the whole Russian Wikipedia](https://mlsanity.com/paper/2204.08009/) (2022)
- [Automatic Summarization of Russian Texts: Comparison of Extractive and Abstractive Methods](https://mlsanity.com/paper/2206.09253/) (2022)
- [Russian-Language Multimodal Dataset for Automatic Summarization of Scientific Papers](https://mlsanity.com/paper/2405.07886/) (2024)

## Abstract (arXiv)

Automatic text summarization has been studied in a variety of domains and languages. However, this does not hold for the Russian language. To overcome this issue, we present Gazeta, the first dataset for summarization of Russian news. We describe the properties of this dataset and benchmark several extractive and abstractive models. We demonstrate that the dataset is a valid task for methods of text summarization for Russian. Additionally, we prove the pretrained mBART model to be useful for Russian text summarization.