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Dataset for Automatic Summarization of Russian News

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Artificial Intelligence and Natural Language (AINL 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1292))

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Abstract

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.

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Notes

  1. 1.

    https://github.com/IlyaGusev/gazeta.

  2. 2.

    https://github.com/IlyaGusev/summarus.

  3. 3.

    https://github.com/kmike/pymorphy2.

  4. 4.

    https://github.com/summanlp/textrank.

  5. 5.

    https://github.com/crabcamp/lexrank.

  6. 6.

    https://github.com/miso-belica/sumy.

  7. 7.

    https://github.com/allenai/allennlp.

  8. 8.

    https://github.com/pytorch/fairseq.

  9. 9.

    https://github.com/huggingface/transformers.

  10. 10.

    https://toloka.yandex.ru/.

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Author information

Authors and Affiliations

  1. Moscow Institute of Physics and Technology, Moscow, Russia

    Ilya Gusev

Authors
  1. Ilya Gusev
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Correspondence to Ilya Gusev .

Editor information

Editors and Affiliations

  1. ITMO University, St. Petersburg, Russia

    Andrey Filchenkov

  2. Haaga-Helia University of Applied Sciences, Helsinki, Finland

    Janne Kauttonen

  3. University of Helsinki, Helsinki, Finland

    Lidia Pivovarova

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Gusev, I. (2020). Dataset for Automatic Summarization of Russian News. In: Filchenkov, A., Kauttonen, J., Pivovarova, L. (eds) Artificial Intelligence and Natural Language. AINL 2020. Communications in Computer and Information Science, vol 1292. Springer, Cham. https://doi.org/10.1007/978-3-030-59082-6_9

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  • DOI: https://doi.org/10.1007/978-3-030-59082-6_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59081-9

  • Online ISBN: 978-3-030-59082-6

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