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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
Both datasets are available from https://cs.rochester.edu/u/nhossain/funlines.html.
- 9.
https://pypi.org/project/truecase/, based on [15].
- 10.
- 11.
PART, CCONJ, SCONJ, ADP, AUX, DET, PRON, PUNCT, or NUM.
- 12.
- 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.
When sampling the sentences, we discarded headlines containing words indicating sensitive topics like violence, death, and religion.
- 15.
We used Cohen’s Kappa with linearly decreasing weights as implemented in Scikit-Learn [21] package.
- 16.
References
Amin, M., Burghardt, M.: A survey on approaches to computational humor generation. In: Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pp. 29–41 (2020)
Annamoradnejad, I., Zoghi, G.: ColBERT: using BERT sentence embedding for humor detection. arXiv preprint arXiv:2004.12765 (2020)
Attardo, S.: Linguistic Theories of Humor. Walter de Gruyter (1994)
Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media, Inc. (2009)
Blinov, V., Mishchenko, K., Bolotova, V., Braslavski, P.: A pinch of humor for short-text conversation: an information retrieval approach. In: International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 3–15 (2017). https://doi.org/10.1007/978-3-319-65813-1_1
Braslavski, P., Blinov, V., Bolotova, V., Pertsova, K.: How to evaluate humorous response generation, seriously? In: Proceedings of the 2018 Conference on Human Information Interaction & Retrieval, pp. 225–228 (2018)
Chen, P.Y., Soo, V.W.: Humor recognition using deep learning. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pp. 113–117 (2018)
Church, K.W., Hanks, P.: Word association norms, mutual information, and lexicography. Comput. Linguist. 16(1), 22–29 (1990)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186 (2019)
Fares, M., Kutuzov, A., Oepen, S., Velldal, E.: Word vectors, reuse, and replicability: Towards a community repository of large-text resources. In: Proceedings of the 21st Nordic Conference on Computational Linguistics, pp. 271–276 (2017)
He, H., Peng, N., Liang, P.: Pun generation with surprise. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1734–1744 (2019)
Horvitz, Z., Do, N., Littman, M.L.: Context-driven satirical news generation. In: Proceedings of the Second Workshop on Figurative Language Processing, pp. 40–50 (2020)
Hossain, N., Krumm, J., Gamon, M.: President vows to cut \(<\)Taxes\(>\) hair: dataset and analysis of creative text editing for humorous headlines. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 133–142 (2019)
Hossain, N., Krumm, J., Sajed, T., Kautz, H.: Stimulating creativity with funlines: a case study of humor generation in headlines. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 256–262 (2020)
Lita, L.V., Ittycheriah, A., Roukos, S., Kambhatla, N.: tRuEcasIng. In: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, pp. 152–159 (2003)
Luo, F., Li, S., Yang, P., Chang, B., Sui, Z., Sun, X., et al.: Pun-GAN: generative adversarial network for pun generation. arXiv preprint arXiv:1910.10950 (2019)
Mihalcea, R., Strapparava, C.: Making computers laugh: investigations in automatic humor recognition. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 531–538 (2005)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Nagy, T.: Raising collocational awareness with humour. Acta Univ. Sapientiae Philologica 12(2), 99–113 (2020)
Partington, A.: ‘Kicking the Habit’: the exploitation of collocation in literature and humour. Linguistic Approaches to Literature, English Language (1995)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Raskin, V.: Semantic Mechanisms of Humor (1985)
Sjöbergh, J., Araki, K.: A measure of funniness, applied to finding funny things in WordNet. In: Proceedings of the Conference of the Pacific Association for Computational Linguistics 2009, pp. 236–241 (2009)
Skalicky, S.: Lexical priming in humorous satirical newspaper headlines. Humor 31(4), 583–602 (2018)
Stock, O., Strapparava, C.: HAHAcronym: a computational humor system. In: ACL (demo), pp. 113–116 (2005)
Valitutti, A., Toivonen, H., Doucet, A., Toivanen, J.M.: Let everything turn well in your wife: generation of adult humor using lexical constraints. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 243–248 (2013)
Weller, O., Fulda, N., Seppi, K.: Can humor prediction datasets be used for humor generation? humorous headline generation via style transfer. In: Proceedings of the Second Workshop on Figurative Language Processing, pp. 186–191 (2020)
Weller, O., Seppi, K.: Humor detection: a transformer gets the last laugh. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, pp. 3621–3625 (2019)
Winters, T., Nys, V., De Schreye, D.: Towards a general framework for humor generation from rated examples. In: Proceedings of the 10th International Conference on Computational Creativity, pp. 274–281 (2019)
Wolf, T., et al.: Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 38–45 (2020)
Yang, D., Lavie, A., Dyer, C., Hovy, E.: Humor recognition and humor anchor extraction. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2367–2376 (2015)
Yu, Z., Tan, J., Wan, X.: A neural approach to pun generation. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1650–1660 (2018)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-16500-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16499-6
Online ISBN: 978-3-031-16500-9
eBook Packages: Computer ScienceComputer Science (R0)