Abstract
Throughout history, human beings have used tales as a way of communicating ideas and transmitting knowledge. One of the great advances in this area was obtained thanks to writing, a fact that substantially facilitated the transmission of these stories. Being such an ancient activity in human history, it is not surprising that it has been the object of study of artificial intelligence, from a very early time in the development of the latter, leading, among other things, to the emergence of the automatic generation of stories. The automatic generation of stories has been a challenge that has been sought to be solved using different approaches. This review brings together some of these guidelines for carrying out this endeavour.
This work was done with support of the Government of Mexico via CONACYT, SNI, CONACYT, BEIFI grant A1-S-47854; grants SIP 2083, SIP 20200811 and SIP 20200859 of the Secretaría de Investigación y Posgrado of the Instituto Politécnico Nacional, Mexico, IPN-COFAA, and IPN-EDI.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Meehan, J.R.: TALE-SPIN, an interactive program that writes stories. In: 5th International Joint Conference on Artificial Intelligence, pp. 91–98 (1977)
Lebowitz, M.: Planning stories. In: 9th Annual Conference of the Cognitive Science Society, pp. 234–242 (1987)
Turner, S.R.: MINSTREL: a computer model of creativity and storytelling. University of California, Computer Science Department (1993)
Pérez, R.P.Y., Sharples, M.: MEXICA: a computer model of a cognitive account of creative writing. J. Exp. Theor. Artif. Intell. 13, 119–139 (2001)
Porteous, J., Cavazza, M.: Controlling narrative generation with planning trajectories: the role of constraints. In: Iurgel, I.A., Zagalo, N., Petta, P. (eds.) Interactive Storytelling. Lecture Notes in Computer Science, vol. 5915, pp. 234–245. Springer, Berlin (2009)
Riedl, M.O., Young, R.M.: Narrative planning: balancing plot and character. J. Artif. Intell. Res. 39, 217–267 (2010)
Onodera, K., Akimoto, T., Ogata, T.: A state-event transformation mechanism for generating micro-structures of story in an integrated narrative generation system. In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 34 (2012)
León, C., Gervás, P.: Creativity in story generation from the ground up: nondeterministic simulation driven by narrative. In: 5th International Conference on Computational Creativity (2014)
McCoy, J., Treanor, M., Samuel, B., Reed, A.A., Mateas, M., Wardrip-Fruin, N.: Social story worlds with comme il faut. IEEE Trans. Comput. Intell. AI Games 6, 97–112 (2014)
Calvo, H., Daza-Arévalo, J.A., Figueroa-Nazuno, J.: Automatic story generation by learning from literary structures. Scholars’ Press (2016)
Calvo, H., Daza-Arévalo, J.A., Figueroa-Nazuno, J.: Automatic text generation by learning from literary structures. In: Proceedings of the Fifth Workshop on Computational Linguistics for Literature, pp. 9–19 (2016)
Adolfo, B.T., Lao, J., Rivera, J.P., Talens, J.Z., Ong, E.C.J.: Generating children’s stories from character and event models. In: Phon-Amnuaisuk, S., Ang, S.P., Lee, S.Y. (eds.) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2017, vol. 10607. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69456-6_22
Farrell, R., Ware, S.G., Baker, L.J.: Manipulating narrative salience in interactive stories using Indexter’s pairwise event salience hypothesis. IEEE Trans. Games 12, 74–85 (2019)
Cardona-Rivera, R.E., Cassell, B.A., Ware, S.G., Young, R.M.: Indexter: a computational model of the event-indexing situation model for characterizing narratives. In: Proceedings of the 3rd Workshop on Computational Models of Narrative, pp. 34–43 (2012)
Propp, V.Y.: Morphology of the Folktale. University of Texas Press, Austin (1968)
Jain, P., Agrawal, P., Mishra, A., Sukhwani, M., Laha, A., Sankaranarayanan, K.: Story generation from sequence of independent short descriptions. In: SIGKDD Workshop on Machine Learning for Creativity (ML4Creativity) (2017)
Clark, E., Ji, Y., Smith, N.A.: Neural text generation in stories using entity representations as context. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 2250–2260 (2018)
Martin, L.J., et al.: Event representations for automated story generation with deep neural nets. In: Thirty-Second AAAI Conference on Artificial Intelligence, pp. 868–875 (2018)
Fan, A., Lewis, M., Dauphin, Y.: Hierarchical neural story generation. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 889–898 (2018)
Peng, N., Ghazvininejad, M., May, J., Knight, K.: Towards controllable story generation. In: Proceedings of the First Workshop on Storytelling, pp. 43–49 (2018)
Roemmele, M., Gordon, A.: Automated assistance for creative writing with an RNN language model. In: Proceedings of the 23rd International Conference on Intelligent User Interfaces Companion (2018)
Xu, J., Ren, X., Zhang, Y., Zeng, Q., Cai, X., Sun, X.: A skeleton-based model for promoting coherence among sentences in narrative story generation. EMNLP (2018)
Li, Z., Ding, X., Liu, T.: Generating reasonable and diversified story ending using sequence to sequence model with adversarial training. In: COLING, pp. 1033–1043 (2018)
Yao, L., Peng, N., Weischedel, R., Knight, K., Zhao, D., Yan, R.: Plan-and-write: towards better automatic storytelling. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (2019)
Luo, F., et al.: Learning to control the fine-grained sentiment for story ending generation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 6020–6026 (2019)
Tambwekar, P., Dhuliawala, M., Martin, L.J., Mehta, A., Harrison, B., Riedl, M.O.: Controllable neural story plot generation via reward shaping. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (2019)
Guan, J., Wang, Y., Huang, M.: Story ending generation with incremental encoding and commonsense knowledge. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 6473–6480 (2019)
Ammanabrolu, P., et al.: Story realization: expanding plot events into sentences. arXiv preprint arXiv:1909.03480 (2019)
Li, J., Bing, L., Qiu, L., Chen, D., Zhao, D., Yan, R.: Learning to write stories with thematic consistency and wording novelty. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1715–1722 (2019)
Fan, A., Lewis, M., Dauphin, Y.: Strategies for structuring story generation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 2650–2660 (2019)
Guan, J., Huang, F., Zhao, Z., Zhu, X., Huang, M.: A knowledge-enhanced pretraining model for commonsense story generation. arXiv preprint arXiv:2001.05139 (2020)
Ippolito, D., Grangier, D., Eck, D., Callison-Burch, C.: Toward better storylines with sentence-level language models. arXiv preprint arXiv:2005.05255 (2020)
Hashimoto, T.B., Guu, K., Oren, Y., Liang, P.: A retrieve-and-edit framework for predicting structured outputs. In: 32nd Conference on Neural Information Processing Systems (2018)
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019)
Devlin, J., Chang, M., 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, vol. 1, pp. 4171–4186 (2019)
Papineni, K., Roukos, S., Ward, T., Zhu, W.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 311–318 (2002)
Shao, Z., Huang, M., Wen, J., Xu, W., Zhu, X.: Long and diverse text generation with planning-based hierarchical variational model. EMNLP (2019)
Li, J., Galley, M., Brockett, C., Gao, J., Dolan B.: A diversity promoting objective function for neural conversation models. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 110–119 (2016)
Acevedo-Mosqueda, M.E., Yáñez-Márquez, C., López-Yáñez, I.: Alpha-beta bidirectional associative memories: theory and applications. Neural Process. Lett. 26(1), 1–40 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Herrera-González, B.D., Gelbukh, A., Calvo, H. (2020). Automatic Story Generation: State of the Art and Recent Trends. In: Martínez-Villaseñor, L., Herrera-Alcántara, O., Ponce, H., Castro-Espinoza, F.A. (eds) Advances in Computational Intelligence. MICAI 2020. Lecture Notes in Computer Science(), vol 12469. Springer, Cham. https://doi.org/10.1007/978-3-030-60887-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-60887-3_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60886-6
Online ISBN: 978-3-030-60887-3
eBook Packages: Computer ScienceComputer Science (R0)