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Jokingbird: Funny Headline Generation for News

  • Conference paper
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Analysis of Images, Social Networks and Texts (AIST 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13217))

Abstract

In this study, we address the problem of generating funny headlines for news articles. Funny headlines are beneficial even for serious news stories – they attract and entertain the reader. Automatically generated funny headlines can serve as prompts for news editors. More generally, humor generation can be applied to other domains, e.g. conversational systems. Like previous approaches, our methods are based on lexical substitutions. We consider two techniques for generating substitute words: one based on BERT and another based on collocation strength and semantic distance. At the final stage, a humor classifier chooses the funniest variant from the generated pool. An in-house evaluation of 200 generated headlines showed that the BERT-based model produces the funniest and in most cases grammatically correct output.

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Notes

  1. 1.

    https://www.theonion.com/.

  2. 2.

    https://components.one/datasets/above-the-fold/.

  3. 3.

    https://www.kaggle.com/snapcrack/all-the-news.

  4. 4.

    https://doi.org/10.7910/DVN/GMFCTR.

  5. 5.

    https://www.reddit.com/.

  6. 6.

    https://www.kaggle.com/abhinavmoudgil95/short-jokes.

  7. 7.

    http://www.statmt.org/wmt16/translation-task.html.

  8. 8.

    Both datasets are available from https://cs.rochester.edu/u/nhossain/funlines.html.

  9. 9.

    https://pypi.org/project/truecase/, based on [15].

  10. 10.

    https://spacy.io/.

  11. 11.

    PART, CCONJ, SCONJ, ADP, AUX, DET, PRON, PUNCT, or NUM.

  12. 12.

    https://github.com/bjascob/LemmInflect.

  13. 13.

    ‘Successful’ sentences are those, where at least one candidate word for replacement was found and at least one generated replacement passed the model’s restrictions on the predicted word.

  14. 14.

    When sampling the sentences, we discarded headlines containing words indicating sensitive topics like violence, death, and religion.

  15. 15.

    We used Cohen’s Kappa with linearly decreasing weights as implemented in Scikit-Learn [21] package.

  16. 16.

    https://toloka.ai/.

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Acknowledgments

The described experiments were partly conducted using HPC facilities of the HSE University. We thank Daria Overnikova for useful comments and suggestions on a draft of this paper. Pavel Braslavski thanks Exactpro company (https://exactpro.com/) for supporting the project.

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Authors and Affiliations

  1. HSE University, Moscow, Russia

    Nikita Login, Alexander Baranov & Pavel Braslavski

  2. Ural Federal University, Yekaterinburg, Russia

    Pavel Braslavski

Authors
  1. Nikita Login
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  2. Alexander Baranov
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  3. Pavel Braslavski
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Corresponding author

Correspondence to Pavel Braslavski .

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

    Evgeny Burnaev

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

    Dmitry I. Ignatov

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

    Sergei Ivanov

  4. Krasovskii Institute of Mathematics and Mechanics of Russian Academy of Sciences, Yekaterinburg, Russia

    Michael Khachay

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

    Olessia Koltsova

  6. University of Oslo, Oslo, Norway

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

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  8. Lomonosov Moscow State University, Moscow, Russia

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  9. LORIA, Campus Scientifique, Vandœuvre lès Nancy, France

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

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  11. University of Florida, Gainesville, USA

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  12. Aalto University, Espoo, Finland

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

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  14. Yandex LLC, Moscow, Russia

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  15. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

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Login, N., Baranov, A., Braslavski, P. (2022). Jokingbird: Funny Headline Generation for News. In: Burnaev, E., et al. Analysis of Images, Social Networks and Texts. AIST 2021. Lecture Notes in Computer Science, vol 13217. Springer, Cham. https://doi.org/10.1007/978-3-031-16500-9_9

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  • DOI: https://doi.org/10.1007/978-3-031-16500-9_9

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  • Online ISBN: 978-3-031-16500-9

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