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Generating Sport Summaries: A Case Study for Russian

  • Conference paper
  • First Online:
Analysis of Images, Social Networks and Texts (AIST 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12602))

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Abstract

We present a novel dataset of sports broadcasts with 8,781 games. The dataset contains 700 thousand comments and 93 thousand related news documents in Russian. We run an extensive series of experiments of modern extractive and abstractive approaches. The results demonstrate that BERT-based models show modest performance, reaching up to 0.26 ROUGE-1F-measure. In addition, human evaluation shows that neural approaches could generate feasible although inaccurate news basing on broadcast text.

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Notes

  1. 1.

    The owner of the dataset approved its publication, so it will be released shortly after the paper is published.

  2. 2.

    https://github.com/google-research/bert/blob/master/multilingual.md.

  3. 3.

    https://nlpub.mipt.ru/Russian_Distributional_Thesaurus.

  4. 4.

    http://bit.ly/diploma_pagerank.

  5. 5.

    https://radimrehurek.com/gensim/index.html.

  6. 6.

    https://github.com/DenisOgr/lexrank/pull/1/files.

  7. 7.

    https://pypi.org/project/lexrank/.

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Acknowledgements

The work of the first author was funded by RFBR, project number 19-37-60027. The final work on the manuscript carried out by Elena Tutubalina was funded by the framework of the HSE University Basic Research Program and Russian Academic Excellence Project “5–100”.

Author information

Authors and Affiliations

  1. Kazan Federal University, Kazan, Russian Federation

    Valentin Malykh & Elena Tutubalina

  2. Ukrainian Catholic University, Lviv, Ukraine

    Denis Porplenko

  3. National Research University Higher School of Economics, Moscow, Russian Federation

    Elena Tutubalina

Authors
  1. Valentin Malykh
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  2. Denis Porplenko
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  3. Elena Tutubalina
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Corresponding author

Correspondence to Valentin Malykh .

Editor information

Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Wil M. P. van der Aalst

  2. University of Ljubljana, Ljubljana, Slovenia

    Vladimir Batagelj

  3. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  4. Krasovskii Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Michael Khachay

  5. National Research University Higher School of Economics, St. Petersburg, Russia

    Olessia Koltsova

  6. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  7. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  8. National Research University Higher School of Economics, Moscow, Russia

    Irina A. Lomazova

  9. Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  10. LORIA, Vandœuvre lès Nancy, France

    Amedeo Napoli

  11. Skolkovo Institute of Science and Technology, Moscow, Russia

    Alexander Panchenko

  12. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  13. Università Ca' Foscari Venezia, Venice, Italy

    Marcello Pelillo

  14. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  15. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

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Malykh, V., Porplenko, D., Tutubalina, E. (2021). Generating Sport Summaries: A Case Study for Russian. In: van der Aalst, W.M.P., et al. Analysis of Images, Social Networks and Texts. AIST 2020. Lecture Notes in Computer Science(), vol 12602. Springer, Cham. https://doi.org/10.1007/978-3-030-72610-2_11

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  • DOI: https://doi.org/10.1007/978-3-030-72610-2_11

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