skip to main content
research-article

Automatic Story Generation: A Survey of Approaches

Published:25 May 2021Publication History
Skip Abstract Section

Abstract

Computational generation of stories is a subfield of computational creativity where artificial intelligence and psychology intersect to teach computers how to mimic humans’ creativity. It helps generate many stories with minimum effort and customize the stories for the users’ education and entertainment needs. Although the automatic generation of stories started to receive attention many decades ago, advances in this field to date are less than expected and suffer from many limitations. This survey presents an extensive study of research in the area of non-interactive textual story generation, as well as covering resources, corpora, and evaluation methods that have been used in those studies. It also shed light on factors of story interestingness.

References

  1. H. Porter Abbott. 2008. The Cambridge Introduction to Narrative. Cambridge University Press.Google ScholarGoogle Scholar
  2. Emily Ahn, Fabrizio Morbini, and Andrew Gordon. 2016. Improving fluency in narrative text generation with grammatical transformations and probabilistic parsing. In Proceedings of the 9th International Natural Language Generation Conference. 70--73.Google ScholarGoogle ScholarCross RefCross Ref
  3. Taisuke Akimoto. 2016. Exploratory approach to the computational modeling of narrative ability for artificial intelligence. International Journal of Knowledge Engineering 2, 4 (2016), 170--176.Google ScholarGoogle ScholarCross RefCross Ref
  4. Nader Akoury, Shufan Wang, Josh Whiting, Stephen Hood, Nanyun Peng, and Mohit Iyyer. 2020. STORIUM: A dataset and platform for human-in-the-loop story generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. 6470--6484.Google ScholarGoogle Scholar
  5. James Allan, Ron Papka, and Victor Lavrenko. 1998. On-line new event detection and tracking. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 37--45.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Prithviraj Ammanabrolu, Ethan Tien, Wesley Cheung, Zhaochen Luo, William Ma, Lara Martin, and Mark Riedl. 2019. Guided neural language generation for automated storytelling. In Proceedings of the 2nd Workshop on Storytelling. 46--55.Google ScholarGoogle ScholarCross RefCross Ref
  7. Prithviraj Ammanabrolu, Ethan Tien, Wesley Cheung, Zhaochen Luo, William Ma, Lara J. Martin, and Mark Riedl. 2020. Story realization: Expanding plot events into sentences. In Proceedings of the 34th AAAI Conference on Artificial Intelligence. 7375--7382.Google ScholarGoogle ScholarCross RefCross Ref
  8. Karen Ang and Ethel Ong. 2011. Enhancing event-based semantics in the ontology of picture books 2. In Proceedings of the 8th National Natural Language Processing Research Symposium. 81--84.Google ScholarGoogle Scholar
  9. Karen Ang and Ethel Ong. 2012. Planning children’s stories using agent models. In Proceedings of the Pacific Rim Knowledge Acquisition Workshop. 195--208.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Kemal Araz. 2020. Transformer Neural Networks for Automated Story Generation. Master’s Thesis. Technological University Dublin, Ireland.Google ScholarGoogle Scholar
  11. Ruth Aylett. 1999. Narrative in virtual environments—Towards emergent narrative. In Proceedings of the AAAI 1999 Fall Symposium on Narrative Intelligence. 83--86.Google ScholarGoogle Scholar
  12. Ruth Aylett, Sandy Louchart, and Allan Weallans. 2011. Research in interactive drama environments, role-play and story-telling. In Proceedings of the International Conference on Interactive Digital Storytelling. 1--12.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Ruth Aylett, Marco Vala, Pedro Sequeira, and Ana Paiva. 2007. FearNot! – An emergent narrative approach to virtual dramas for anti-bullying education. In Proceedings of the International Conference on Virtual Storytelling. 202--205.Google ScholarGoogle ScholarCross RefCross Ref
  14. Byung-Chull Bae, Yun-Gyung Cheong, and R. Michael Young. 2011. Toward a computational model of focalization in narrative. In Proceedings of the 6th International Conference on Foundations of Digital Games. 313--315.Google ScholarGoogle Scholar
  15. Byung-Chull Bae and R. Michael Young. 2008. A use of flashback and foreshadowing for surprise arousal in narrative using a plan-based approach. In Proceedings of the Joint International Conference on Interactive Digital Storytelling. 156--167.Google ScholarGoogle Scholar
  16. Byung-Chull Bae and R. Michael Young. 2013. A computational model of narrative generation for surprise arousal. IEEE Transactions on Computational Intelligence and AI in Games 6, 2 (2013), 131--143.Google ScholarGoogle ScholarCross RefCross Ref
  17. Paul Bailey. 1999. Searching for storiness: Story-generation from a reader’s perspective. In Working Notes of the Narrative Intelligence Symposium. 157--164.Google ScholarGoogle Scholar
  18. Niranjan Balasubramanian, Stephen Soderland, Mausam, and Oren Etzioni. 2012. Rel-grams: A probabilistic model of relations in text. In Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction. 101--105.Google ScholarGoogle Scholar
  19. Niranjan Balasubramanian, Stephen Soderland, Mausam, and Oren Etzioni. 2013. Generating coherent event schemas at scale. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 1721--1731.Google ScholarGoogle Scholar
  20. Regina Barzilay and Mirella Lapata. 2008. Modeling local coherence: An entity-based approach. Computational Linguistics 34, 1 (2008), 1--34.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Morteza Behrooz, Justus Robertson, and Arnav Jhala. 2019. Investigating the use of word embeddings to estimate cognitive interest in stories. In Proceedings of the 41st Annual Conference of the Cognitive Science Society (CogSci’19), A. K. Goel, C.M. Seifert, and C. Freksa (Eds.). Cognitive Science Society, 1388--1394.Google ScholarGoogle Scholar
  22. Morteza Behrooz, Justus Robertson, and Arnav Jhala. 2019. Story quality as a matter of perception: Using word embeddings to estimate cognitive interest. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, Vol. 15. 3--9.Google ScholarGoogle Scholar
  23. John Black and Gordon Bower. 1980. Story understanding as problem-solving. Poetics 9, 1--3 (1980), 223--250.Google ScholarGoogle ScholarCross RefCross Ref
  24. John Black and Robert Wilensky. 1979. An evaluation of story grammars. Cognitive Science 3, 3 (1979), 213--229.Google ScholarGoogle ScholarCross RefCross Ref
  25. Margaret Boden. 2004. The Creative Mind: Myths and Mechanisms. Routledge.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Margaret Boden. 2009. Computer models of creativity. AI Magazine 30, 3 (2009), 23.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Kevin Bowden, Grace Lin, Lena Reed, Jean Tree, and Marilyn Walker. 2016. M2D: Monolog to dialog generation for conversational story telling. In Proceedings of the International Conference on Interactive Digital Storytelling. 12--24.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Elizabeth Bowen. 1945. Notes on writing a novel. Orion 2 (1945), 18--30.Google ScholarGoogle Scholar
  29. Margaret Bradley and Peter Lang. 1999. Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings. Technical Report C-1. Center for Research in Psychophysiology, University of Florida.Google ScholarGoogle Scholar
  30. William Brewer and Edward Lichtenstein. 1982. Stories are to entertain: A structural-affect theory of stories. Journal of Pragmatics 6, 5--6 (1982), 473--486.Google ScholarGoogle ScholarCross RefCross Ref
  31. Selmer Bringsjord and David Ferrucci. 1999. Artificial Intelligence and Literary Creativity: Inside the Mind of Brutus, a Storytelling Machine. Psychology Press.Google ScholarGoogle Scholar
  32. Selmer Bringsjord and Dave Ferrucci. 1999. BRUTUS and the narrational case against church’s thesis. In Proceedings of the AAAI 1999 Fall Symposium on Narrative Intelligence. 105--111.Google ScholarGoogle Scholar
  33. Martin Burget. 2013. Works of Alfred Hitchcock: An Analysis. Master’s Thesis. Filozofická fakulta, Masarykova Univerzita, Brno, Czech Republic.Google ScholarGoogle Scholar
  34. Sandra Carberry. 2001. Techniques for plan recognition. User Modeling and User-Adapted Interaction 11, 1--2 (2001), 31--48.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Tommaso Caselli and Piek Vossen. 2017. The event storyline corpus: A new benchmark for causal and temporal relation extraction. In Proceedings of the Events and Stories in the News Workshop. 77--86.Google ScholarGoogle ScholarCross RefCross Ref
  36. Nathanael Chambers and Dan Jurafsky. 2008. Unsupervised learning of narrative event chains. In Proceedings of the 46th Association for Computational Linguistics Conference: Human Language Technologies. 789--797.Google ScholarGoogle Scholar
  37. Nathanael Chambers and Dan Jurafsky. 2009. Unsupervised learning of narrative schemas and their participants. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, Vol. 2. 602--610.Google ScholarGoogle ScholarCross RefCross Ref
  38. Nathanael Chambers, Shan Wang, and Dan Jurafsky. 2007. Classifying temporal relations between events. In Proceedings of the 45th Annual Meeting of the ACL: Interactive Poster and Demonstration Sessions. 173--176.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Snigdha Chaturvedi, Haoruo Peng, and Dan Roth. 2017. Story comprehension for predicting what happens next. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 1603--1614.Google ScholarGoogle ScholarCross RefCross Ref
  40. Gang Chen, Yang Liu, Huanbo Luan, Meng Zhang, Qun Liu, and Maosong Sun. 2020. Learning to generate explainable plots for neural story generation. IEEE/ACM Transactions on Audio, Speech, and Language Processing 29 (2020), 585--593.Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Jiaao Chen, Jianshu Chen, and Zhou Yu. 2019. Incorporating structured commonsense knowledge in story completion. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 6244--6251.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Yun-Gyung Cheong and R. Michael Young. 2014. Suspenser: A story generation system for suspense. IEEE Transactions on Computational Intelligence and AI in Games 7, 1 (2014), 39--52.Google ScholarGoogle ScholarCross RefCross Ref
  43. Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of 2014 Conference on Empirical Methods in Natural Language Processing. 1724--1734.Google ScholarGoogle ScholarCross RefCross Ref
  44. YunSeok Choi, SuAh Kim, and Jee-Hyong Lee. 2016. Recurrent neural network for storytelling. In Proceedings of the 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems and 17th International Symposium on Advanced Intelligent Systems. 841--845.Google ScholarGoogle ScholarCross RefCross Ref
  45. Fellbaum Christiane (Ed.). 1998. WordNet: An Electronic Lexical Database. MIT Press, Cambridge, MA.Google ScholarGoogle Scholar
  46. William Cook. 2011. Plotto: The Master Book of All Plots. Tin House Books.Google ScholarGoogle Scholar
  47. Natlie Dehn. 1981. Story generation after TALE-SPIN. In Proceedings of the 7th International Joint Conference on Artificial Intelligence, Vol. 1. 16--18.Google ScholarGoogle Scholar
  48. Pablo Delatorre, Barbara Arfe, Pablo Gervás, and Manuel Palomo-Duarte. 2016. A component-based architecture for suspense modelling. In Proceedings of the 3rd AISB Symposium on Computational Creativity. 32--39.Google ScholarGoogle Scholar
  49. Pablo Delatorre, Carlos León, Pablo Gervás, and Manuel Palomo-Duarte. 2017. A computational model of the cognitive impact of decorative elements on the perception of suspense. Connection Science 29, 4 (2017), 295--331.Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Pablo Delatorre, Carlos León, Alberto Salguero, Manuel Palomo-Duarte, and Pablo Gervás. 2018. Confronting a paradox: A new perspective of the impact of uncertainty in suspense. Frontiers in Psychology 9 (2018), 1392.Google ScholarGoogle ScholarCross RefCross Ref
  51. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. 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. 4171--4186.Google ScholarGoogle Scholar
  52. Jesse Dunietz, Lori Levin, and Jaime G. Carbonell. 2017. The BECauSE corpus 2.0: Annotating causality and overlapping relations. In Proceedings of the 11th Linguistic Annotation Workshop. 95--104.Google ScholarGoogle Scholar
  53. Markus Eger, Colin Potts, Camille Barot, and R. Michael Young. 2015. Plotter: Operationalizing the master book of all plots. In Proceedings of the Conference on Intelligent Narrative Technologies and Social Believability in Games. 30--33.Google ScholarGoogle Scholar
  54. Angela Fan, Mike Lewis, and Yann Dauphin. 2018. Hierarchical neural story generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Vol. 1. 889--898.Google ScholarGoogle ScholarCross RefCross Ref
  55. Angela Fan, Mike Lewis, and Yann Dauphin. 2019. Strategies for structuring story generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2650--2660.Google ScholarGoogle ScholarCross RefCross Ref
  56. Michael Flor and Swapna Somasundaran. 2017. Sentiment analysis and lexical cohesion for the story cloze task. In Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential, and Discourse-level Semantics. 62--67.Google ScholarGoogle ScholarCross RefCross Ref
  57. Gustav Freytag. 1968. The Technique of the Drama: An Exposition of Dramatic Composition and Art. B. Blom, New York, NY.Google ScholarGoogle Scholar
  58. Gérard Genette. 1983. Narrative Discourse: An Essay in Method. Vol. 3. Cornell University Press.Google ScholarGoogle Scholar
  59. Richard Gerrig and Allan Bernardo. 1994. Readers as problem-solvers in the experience of suspense. Poetics 22, 6 (1994), 459--472.Google ScholarGoogle ScholarCross RefCross Ref
  60. Pablo Gervás. 2009. Computational approaches to storytelling and creativity. AI Magazine 30, 3 (2009), 49.Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Pablo Gervás, Belén Díaz-Agudo, Federico Peinado, and Raquel Hervás. 2004. Story plot generation based on CBR. In Proceedings of the International Conference on Innovative Techniques and Applications of Artificial Intelligence. 33--46.Google ScholarGoogle Scholar
  62. Johann Goethe. 1971. Elective Affinities. Penguin UK.Google ScholarGoogle Scholar
  63. Mark Granroth-Wilding and Stephen Clark. 2016. What happens next? Event prediction using a compositional neural network model. In Proceedings of the 30th AAAI Conference on Artificial Intelligence.Google ScholarGoogle Scholar
  64. Jian Guan, Fei Huang, Zhihao Zhao, Xiaoyan Zhu, and Minlie Huang. 2020. A knowledge-enhanced pretraining model for commonsense story generation. Transactions of the Association for Computational Linguistics 8 (2020), 93--108.Google ScholarGoogle ScholarCross RefCross Ref
  65. Jian Guan, Yansen Wang, and Minlie Huang. 2019. Story ending generation with incremental encoding and commonsense knowledge. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence, Vol. 33. 6473--6480.Google ScholarGoogle ScholarDigital LibraryDigital Library
  66. B. Harrison, C. Purdy, and M. Riedl. 2017. Toward automated story generation with Markov chain Monte Carlo methods and deep neural networks. In Proceedings of the 13th Artificial Intelligence and Interactive Digital Entertainment Conference. 191--197.Google ScholarGoogle Scholar
  67. Ken Hartsook, Alexander Zook, Sauvik Das, and Mark Riedl. 2011. Toward supporting stories with procedurally generated game worlds. In Proceedings of the IEEE Conference on Computational Intelligence and Games. 297--304.Google ScholarGoogle ScholarCross RefCross Ref
  68. Patrik Haslum. 2012. Narrative planning: Compilations to classical planning. Journal of Artificial Intelligence Research 44 (2012), 383--395.Google ScholarGoogle ScholarDigital LibraryDigital Library
  69. Hans Hoeken and Mario van Vliet. 2000. Suspense, curiosity, and surprise: How discourse structure influences the affective and cognitive processing of a story. Poetics 27, 4 (2000), 277--286.Google ScholarGoogle ScholarCross RefCross Ref
  70. Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019. The curious case of neural text degeneration. In Proceedings of the International Conference on Learning Representations.Google ScholarGoogle Scholar
  71. Ari Holtzman, Jan Buys, Maxwell Forbes, Antoine Bosselut, David Golub, and Yejin Choi. 2018. Learning to write with cooperative discriminators. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Vol. 1. 1638--1649.Google ScholarGoogle ScholarCross RefCross Ref
  72. Linmei Hu, Juanzi Li, Liqiang Nie, Xiao-Li Li, and Chao Shao. 2017. What happens next? Future subevent prediction using contextual hierarchical LSTM. In Proceedings of the 31st AAAI Conference on Artificial Intelligence.Google ScholarGoogle Scholar
  73. Sheng-Hao Hung, Chia-Hung Lin, and Jen-Shin Hong. 2010. Web mining for event-based commonsense knowledge using lexico-syntactic pattern matching and semantic role labeling. Expert Systems with Applications 37, 1 (2010), 341--347.Google ScholarGoogle ScholarDigital LibraryDigital Library
  74. Daphne Ippolito, David Grangier, Douglas Eck, and Chris Callison-Burch. 2020. Toward better storylines with sentence-level language models. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 7472--7478.Google ScholarGoogle ScholarCross RefCross Ref
  75. Parag Jain, Priyanka Agrawal, Abhijit Mishra, Mohak Sukhwani, Anirban Laha, and Karthik Sankaranarayanan. 2017. Story generation from sequence of independent short descriptions. In Proceedings of the SIGKDD Workshop on Machine Learning for Creativity (ML4Creativity’17).Google ScholarGoogle Scholar
  76. Bram Jans, Steven Bethard, Ivan Vulić, and Marie Francine Moens. 2012. Skip n-grams and ranking functions for predicting script events. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics. 336--344.Google ScholarGoogle ScholarDigital LibraryDigital Library
  77. A. Jaya and G. V. Uma. 2010. An intelligent system for semi-automatic story generation for kids using ontology. In Proceedings of the 3rd Annual ACM Bangalore Conference. Article 8, 6 pages.Google ScholarGoogle Scholar
  78. Arnav Jhala and R. Michael Young. 2011. Intelligent machinima generation for visual storytelling. In Artificial Intelligence for Computer Games. Springer, 151--170.Google ScholarGoogle Scholar
  79. Anna Jordanous. 2012. A standardised procedure for evaluating creative systems: Computational creativity evaluation based on what it is to be creative. Cognitive Computation 4, 3 (2012), 246--279.Google ScholarGoogle ScholarCross RefCross Ref
  80. Bilal Kartal, John Koenig, and Stephen Guy. 2014. User-driven narrative variation in large story domains using Monte Carlo tree search. In Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems. 69--76.Google ScholarGoogle ScholarDigital LibraryDigital Library
  81. U. Khandelwal, He He, P. Qi, and D. Jurafsky. 2018. Sharp nearby, fuzzy far away: How neural language models use context. In Proceedings of 56th Annual Meeting of the Association for Computational Linguistics, Vol. 1. 284--294.Google ScholarGoogle Scholar
  82. Walter Kintsch. 1980. Learning from text, levels of comprehension, or: Why anyone would read a story anyway. Poetics 9, 1--3 (1980), 87--98.Google ScholarGoogle ScholarCross RefCross Ref
  83. Silvia Knobloch-Westerwick and Caterina Keplinger. 2006. Mystery appeal: Effects of uncertainty and resolution on the enjoyment of mystery. Media Psychology 8, 3 (2006), 193--212.Google ScholarGoogle ScholarCross RefCross Ref
  84. Ben Kybartas and Rafael Bidarra. 2016. A survey on story generation techniques for authoring computational narratives. IEEE Transactions on Computational Intelligence and AI in Games 9, 3 (2016), 239--253.Google ScholarGoogle ScholarCross RefCross Ref
  85. George Lakoff. 1972. Structural complexity in fairy tales. Study of Man 1 (1972), 128--150.Google ScholarGoogle Scholar
  86. Michael Lebowitz. 1983. Creating a Story-telling Universe. Technical Report CUCS-055-83. Department of Computer Science, Columbia University. https://doi.org/10.7916/D8RV0WQNGoogle ScholarGoogle Scholar
  87. Michael Lebowitz. 1984. Creating characters in a story-telling universe. Poetics 13, 3 (1984), 171--194.Google ScholarGoogle ScholarCross RefCross Ref
  88. Michael Lebowitz. 1985. Story-telling as planning and learning. Poetics 14, 6 (1985), 483--502.Google ScholarGoogle ScholarCross RefCross Ref
  89. Carlos León and Pablo Gervás. 2010. The role of evaluation-driven rejection in the successful exploration of a conceptual space of stories. Minds and Machines 20, 4 (2010), 615--634.Google ScholarGoogle ScholarDigital LibraryDigital Library
  90. Boyang Li, Stephen Lee-Urban, George Johnston, and Mark Riedl. 2013. Story generation with crowdsourced plot graphs. In Proceedings of the 27th AAAI Conference on Artificial Intelligence. 598--604.Google ScholarGoogle Scholar
  91. Qian Li, Ziwei Li, Jin-Mao Wei, Yanhui Gu, Adam Jatowt, and Zhenglu Yang. 2018. A multi-attention based neural network with external knowledge for story ending predicting task. In Proceedings of the 27th International Conference on Computational Linguistics. 1754--1762.Google ScholarGoogle Scholar
  92. Zhongyang Li, Xiao Ding, and Ting Liu. 2018. Generating reasonable and diversified story ending using sequence to sequence model with adversarial training. In Proceedings of the 27th International Conference on Computational Linguistics. 1033--1043.Google ScholarGoogle Scholar
  93. Zhongyang Li, Xiao Ding, and Ting Liu. 2019. Story ending prediction by transferable BERT. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 1800--1806.Google ScholarGoogle ScholarCross RefCross Ref
  94. Zhongyang Li, Xiao Ding, Ting Liu, J. Edward Hu, and Benjamin Van Durme. 2020. Guided generation of cause and effect. In Proceedings of the 29th International Joint Conference on Artificial Intelligence. 3629--3636.Google ScholarGoogle ScholarCross RefCross Ref
  95. Hongyu Lin, Le Sun, and Xianpei Han. 2017. Reasoning with heterogeneous knowledge for commonsense machine comprehension. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2032--2043.Google ScholarGoogle ScholarCross RefCross Ref
  96. Chia-Wei Liu, Ryan Lowe, Iulian Vlad Serban, Mike Noseworthy, Laurent Charlin, and Joelle Pineau. 2016. How NOT To Evaluate Your Dialogue System: An empirical study of unsupervised evaluation metrics for dialogue response generation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2122--2132.Google ScholarGoogle ScholarCross RefCross Ref
  97. Hugo Liu and Push Singh. 2002. MAKEBELIEVE: Using commonsense knowledge to generate stories. In Proceedings of the 18th National Conference on Artificial Intelligence (AAAI’02). 957--958.Google ScholarGoogle Scholar
  98. Hugo Liu and Push Singh. 2004. ConceptNet—A practical commonsense reasoning tool-kit. BT Technology Journal 22, 4 (2004), 211--226.Google ScholarGoogle ScholarDigital LibraryDigital Library
  99. Vincenzo Lombardo and Rossana Damiano. 2012. Storytelling on mobile devices for cultural heritage. New Review of Hypermedia and Multimedia 18, 1--2 (2012), 11--35.Google ScholarGoogle ScholarDigital LibraryDigital Library
  100. Stephanie Lukin and Marilyn Walker. 2015. Narrative variations in a virtual storyteller. In Proceedings of the International Conference on Intelligent Virtual Agents. 320--331.Google ScholarGoogle ScholarCross RefCross Ref
  101. Stephanie Lukin and Marilyn Walker. 2019. A narrative sentence planner and structurer for domain independent, parameterizable storytelling. Dialogue & Discourse 10, 1 (2019), 34--86.Google ScholarGoogle ScholarCross RefCross Ref
  102. Shuming Ma and Xu Sun. 2017. A semantic relevance based neural network for text summarization and text simplification. arXiv:1710.02318Google ScholarGoogle Scholar
  103. Enrique Manjavacas, Folgert Karsdorp, Ben Burtenshaw, and Mike Kestemont. 2017. Synthetic literature: Writing science fiction in a co-creative process. In Proceedings of the Workshop on Computational Creativity in Natural Language Generation. 29--37.Google ScholarGoogle ScholarCross RefCross Ref
  104. Pierre Maranda. 1985. Semiography and artificial intelligence. International Semiotic Spectrum 4 (1985), 1--3.Google ScholarGoogle Scholar
  105. Lara Martin, Prithviraj Ammanabrolu, Xinyu Wang, William Hancock, Shruti Singh, Brent Harrison, and Mark Riedl. 2018. Event representations for automated story generation with deep neural nets. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 868--875.Google ScholarGoogle Scholar
  106. Michael Mateas and Phoebe Sengers (Eds.). 2003. Narrative Intelligence. John Benjamins Publishing.Google ScholarGoogle Scholar
  107. Michael Mateas and Andrew Stern. 2003. Integrating plot, character and natural language processing in the interactive drama Façade. In Proceedings of the 1st International Conference on Technologies for Interactive Digital Storytelling and Entertainment, Vol. 2.Google ScholarGoogle Scholar
  108. Neil McIntyre and Mirella Lapata. 2010. Plot induction and evolutionary search for story generation. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. 1562--1572.Google ScholarGoogle Scholar
  109. James Meehan. 1977. TALE-SPIN, an interactive program that writes stories. In Proceedings of the 5th International Joint Conference on Artificial intelligence, Vol. 1. 91--98.Google ScholarGoogle Scholar
  110. Gonzalo Méndez, Pablo Gervás, and Carlos León. 2016. On the use of character affinities for story plot generation. In Knowledge, Information and Creativity Support Systems. 211--225.Google ScholarGoogle Scholar
  111. Paramita Mirza and Sara Tonelli. 2014. An analysis of causality between events and its relation to temporal information. In Proceedings of the 25th International Conference on Computational Linguistics. 2097--2106.Google ScholarGoogle Scholar
  112. Paramita Mirza and Sara Tonelli. 2016. Catena: Causal and temporal relation extraction from natural language texts. In Proceedings of the 26th International Conference on Computational Linguistics. 64--75.Google ScholarGoogle Scholar
  113. Yusuke Mori, Hiroaki Yamane, Yoshitaka Ushiku, and Tatsuya Harada. 2019. How narratives move your mind: A corpus of shared-character stories for connecting emotional flow and interestingness. Information Processing & Management 56, 5 (2019), 1865--1879.Google ScholarGoogle ScholarDigital LibraryDigital Library
  114. Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen. 2016. A corpus and cloze evaluation for deeper understanding of commonsense stories. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 839--849.Google ScholarGoogle ScholarCross RefCross Ref
  115. Nasrin Mostafazadeh, Alyson Grealish, Nathanael Chambers, James Allen, and Lucy Vanderwende. 2016. CaTeRS: Causal and temporal relation scheme for semantic annotation of event structures. In Proceedings of the 4th Workshop on Events. 51--61.Google ScholarGoogle ScholarCross RefCross Ref
  116. Nasrin Mostafazadeh, Lucy Vanderwende, Wen-Tau Yih, Pushmeet Kohli, and James Allen. 2016. Story cloze evaluator: Vector space representation evaluation by predicting what happens next. In Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP. 24--29.Google ScholarGoogle ScholarCross RefCross Ref
  117. Mark Nelson and Michael Mateas. 2005. Search-based drama management in the interactive fiction anchorhead. In Proceedings of the 1st Artificial Intelligence and Interactive Digital Entertainment Conference. 99--104.Google ScholarGoogle Scholar
  118. Qiang Ning, Zhili Feng, Hao Wu, and Dan Roth. 2018. Joint reasoning for temporal and causal relations. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Vol. 1. 2278--2288.Google ScholarGoogle ScholarCross RefCross Ref
  119. Takashi Ogata. 2016. Computational and cognitive approaches to narratology from the perspective of narrative generation. In Computational and Cognitive Approaches to Narratology. IGI Global, 1--74.Google ScholarGoogle Scholar
  120. Takashi Ogata and Sayaka Yamakage. 2004. A computational mechanism of the “distance” in narrative: A trial in the expansion of literary theory. In Proceedings of the 8th World Multiconference on Systemics, Cybernetics, and Informatics, Vol. 14. 179--184.Google ScholarGoogle Scholar
  121. Katri Oinonen, Mariët Theune, Anton Nijholt, and Jasper Uijlings. 2006. Designing a story database for use in automatic story generation. In Proceedings of the International Conference on Entertainment Computing. 298--301.Google ScholarGoogle ScholarDigital LibraryDigital Library
  122. Brian O’Neill and Mark Riedl. 2014. Dramatis: A computational model of suspense. In Proceedings of the 28th AAAI Conference on Artificial Intelligence. 944--950.Google ScholarGoogle Scholar
  123. Ethel Ong. 2010. A commonsense knowledge base for generating children’s stories. In Proceedings of the AAAI Fall Symposium Series on Common Sense Knowledge. 82--87.Google ScholarGoogle Scholar
  124. TeongJoo Ong and John Leggett. 2004. A genetic algorithm approach to interactive narrative generation. In Proceedings of the 15th ACM Conference on Hypertext and Hypermedia. ACM, New York, NY, 181--182.Google ScholarGoogle ScholarDigital LibraryDigital Library
  125. Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019. fairseq: A fast, extensible toolkit for sequence modeling. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations). 48--53.Google ScholarGoogle ScholarCross RefCross Ref
  126. Federico Peinado and Pablo Gervás. 2006. Evaluation of automatic generation of basic stories. New Generation Computing 24, 3 (2006), 289--302.Google ScholarGoogle ScholarDigital LibraryDigital Library
  127. Lyn Pemberton. 1984. Story Structure: A Narrative Grammar of Nine Chansons de Geste of the Guillaume d’Orange Cycle. Ph.D. Dissertation. University of Toronto.Google ScholarGoogle Scholar
  128. Lyn Pemberton. 1989. A modular approach to story generation. In Proceedings of the 4th Conference of the European Chapter of the Association for Computational Linguistics. 217--224.Google ScholarGoogle ScholarDigital LibraryDigital Library
  129. Rafael Perez y Perez and Mike Sharples. 2001. MEXICA: A computer model of a cognitive account of creative writing. Journal of Experimental and Theoretical Artificial Intelligence 13, 2 (2001), 119--139.Google ScholarGoogle ScholarCross RefCross Ref
  130. Andreea-Oana Petac, Anne-Gwenn Bosser, Fred Charles, Pierre De Loor, and Marc Cavazza. 2020. A pragmatics-based model for narrative dialogue generation. In Proceedings of the 11th International Conference on Computational Creativity.Google ScholarGoogle Scholar
  131. Karl Pichotta and Raymond Mooney. 2014. Statistical script learning with multi-argument events. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics. 220--229.Google ScholarGoogle ScholarCross RefCross Ref
  132. Karl Pichotta and Raymond Mooney. 2016. Using sentence-level LSTM language models for script inference. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Vol. 1. 279--289.Google ScholarGoogle ScholarCross RefCross Ref
  133. Karl Pichotta and Raymond J. Mooney. 2016. Learning statistical scripts with LSTM recurrent neural networks. In Proceedings of the 30th AAAI Conference on Artificial Intelligence. 2800--2806.Google ScholarGoogle Scholar
  134. Georges Polti. 1916. The Thirty-Six Dramatic Situations, L. Ray (Trans.). The Writer, Boston, MA. First published in French in 1895.Google ScholarGoogle Scholar
  135. Vladimir Propp. 1968. Morphology of the Folktale. University of Texas Press.Google ScholarGoogle Scholar
  136. Christopher Purdy, Xinyu Wang, Larry He, and Mark Riedl. 2018. Predicting generated story quality with quantitative measures. In Proceedings of 14th Conference on Artificial Intelligence and Interactive Digital Entertainment. 95--101.Google ScholarGoogle Scholar
  137. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI Blog 1, 8 (2019), 1--24.Google ScholarGoogle Scholar
  138. Hannah Rashkin, Maarten Sap, Emily Allaway, Noah A. Smith, and Yejin Choi. 2018. Event2Mind: Commonsense inference on events, intents, and reactions. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Vol. 1. 463--473.Google ScholarGoogle ScholarCross RefCross Ref
  139. M. Riedl, A. Stern, D. Dini, and J. Alderman. 2008. Dynamic experience management in virtual worlds for entertainment, education, and training. International Transactions on Systems Science and Applications 4, 1 (2008), 23--42.Google ScholarGoogle Scholar
  140. Mark Riedl and R. Michael Young. 2003. Character-focused narrative generation for execution in virtual worlds. In Proceedings of the International Conference on Virtual Storytelling. 47--56.Google ScholarGoogle Scholar
  141. Mark Riedl and R. Michael Young. 2004. An intent-driven planner for multi-agent story generation. In Proceedings of the 3rd International Joint Conference on Autonomous Agents and Multiagent Systems, Vol. 1. 186--193.Google ScholarGoogle Scholar
  142. Mark Riedl and R. Michael Young. 2010. Narrative planning: Balancing plot and character. Journal of Artificial Intelligence Research 39 (2010), 217--268.Google ScholarGoogle ScholarDigital LibraryDigital Library
  143. Elena Rishes, Stephanie Lukin, David Elson, and Marilyn Walker. 2013. Generating different story tellings from semantic representations of narrative. In Proceedings 6th International Conference on Interactive Storytelling. 192--204.Google ScholarGoogle ScholarDigital LibraryDigital Library
  144. Melissa Roemmele and Andrew Gordon. 2015. Creative help: A story writing assistant. In Proceedings of the International Conference on Interactive Digital Storytelling. 81--92.Google ScholarGoogle ScholarCross RefCross Ref
  145. Melissa Roemmele, Andrew Gordon, and Reid Swanson. 2017. Evaluating story generation systems using automated linguistic analyses. In Proceedings of the SIGKDD 2017 Workshop on Machine Learning for Creativity.Google ScholarGoogle Scholar
  146. Melissa Roemmele, Sosuke Kobayashi, Naoya Inoue, and Andrew Gordon. 2017. An RNN-based binary classifier for the story cloze test. In Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential, and Discourse-level Semantics. 74--80.Google ScholarGoogle ScholarCross RefCross Ref
  147. Stuart Rose, Dave Engel, Nick Cramer, and Wendy Cowley. 2010. Automatic keyword extraction from individual documents. In Text Mining: Applications and Theory, Michael Berry and Jacob Kogan (Eds.). John Wiley & Sons, New York, NY, 3--20.Google ScholarGoogle Scholar
  148. Rachel Rudinger, Pushpendre Rastogi, Francis Ferraro, and Benjamin Van Durme. 2015. Script induction as language modeling. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 1681--1686.Google ScholarGoogle ScholarCross RefCross Ref
  149. David E. Rumelhart. 1975. Notes on a schema for stories. In Representation and Understanding. Studies in Cognitive Science. Morgan Kaufmann, 211--236.Google ScholarGoogle Scholar
  150. James Ryan. 2017. Grimes’ fairy tales: A 1960s story generator. Lecture Notes in Computer Science 10690 (2017), 89--103.Google ScholarGoogle ScholarDigital LibraryDigital Library
  151. Manasvi Sagarkar, John Wieting, Lifu Tu, and Kevin Gimpel. 2018. Quality signals in generated stories. In Proceedings of the 7th Joint Conference on Lexical and Computational Semantics. 192--202.Google ScholarGoogle ScholarCross RefCross Ref
  152. Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, and Yejin Choi. 2019. Atomic: An atlas of machine commonsense for if-then reasoning. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence. 3027--3035.Google ScholarGoogle ScholarDigital LibraryDigital Library
  153. Roger Schank and Robert Abelson. 2013. Scripts, Plans, Goals, and Understanding: An Inquiry into Human Knowledge Structures. Psychology Press.Google ScholarGoogle Scholar
  154. Victoria Schmidt. 2005. Story Structure Architect. Penguin.Google ScholarGoogle Scholar
  155. Gregory Schraw, Terri Flowerday, and Stephen Lehman. 2001. Increasing situational interest in the classroom. Educational Psychology Review 13, 3 (2001), 211--224.Google ScholarGoogle ScholarCross RefCross Ref
  156. Abigail See, Peter Liu, and Christopher Manning. 2017. Get to the point: Summarization with pointer-generator networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vol. 1. 1073--1083.Google ScholarGoogle Scholar
  157. Abigail See, Aneesh Pappu, Rohun Saxena, Akhila Yerukola, and Christopher D. Manning. 2019. Do massively pretrained language models make better storytellers? In Proceedings of the 23rd Conference on Computational Natural Language Learning. 843--861.Google ScholarGoogle Scholar
  158. R. Sharma, J. Allen, O. Bakhshandeh, and N. Mostafazadeh. 2018. Tackling the story ending biases in the story cloze test. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Vol. 2. 752--757.Google ScholarGoogle Scholar
  159. Mike Sharples. 1996. An account of writing as creative design. In The Science of Writing: Theories, Methods, Individual Differences and Applications, C. Michael Levy and Sarah Ransdell (Eds.). Routledge, 127--148.Google ScholarGoogle Scholar
  160. Manvir Singh. 2019. The sympathetic plot, its psychological origins, and implications for the evolution of fiction. OSF Reprints. http://doi.org/10.31219/osf.io/p8q7aGoogle ScholarGoogle Scholar
  161. Push Singh, Thomas Lin, Erik Mueller, Grace Lim, Travell Perkins, and Wan Li Zhu. 2002. Open mind common sense: Knowledge acquisition from the general public. Lecture Notes in Computer Science 2519 (2002), 1223--1237.Google ScholarGoogle ScholarCross RefCross Ref
  162. Candice Solis, Joan Tiffany Siy, Emerald Tabirao, and Ethel Ong. 2009. Planning author and character goals for story generation. In Proceedings of the Workshop on Computational Approaches to Linguistic Creativity. 63--70.Google ScholarGoogle ScholarDigital LibraryDigital Library
  163. Youngrok Song, Hyunju Kim, Taewoo Yoo, Byung-Chull Bae, and Yun-Gyung Cheong. 2020. An intelligent storytelling system for narrative conflict generation and resolution. In Proceedings of the IEEE Conference on Games. 192--197.Google ScholarGoogle ScholarCross RefCross Ref
  164. Von-Wun Soo, Chi-Mou Lee, and Tai-Hsun Chen. 2016. Generate believable causal plots with user preferences using constrained monte carlo tree search. In Proceedings of the 12th Artificial Intelligence and Interactive Digital Entertainment Conference.Google ScholarGoogle Scholar
  165. Ilya Sutskever, Oriol Vinyals, and Quoc Le. 2014. Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems 27 (2014), 3104--3112.Google ScholarGoogle ScholarDigital LibraryDigital Library
  166. Reid Swanson and Andrew S. Gordon. 2012. Say anything: Using textual case-based reasoning to enable open-domain interactive storytelling. ACM Transactions on Interactive Intelligent Systems 2, 3 (2012), 1--35.Google ScholarGoogle ScholarDigital LibraryDigital Library
  167. Pradyumna Tambwekar, Murtaza Dhuliawala, Lara Martin, Animesh Mehta, Brent Harrison, and Mark Riedl. 2019. Controllable neural story plot generation via reward shaping. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 5982--5988.Google ScholarGoogle ScholarCross RefCross Ref
  168. Ed S. Tan. 2013. Emotion and the Structure of Narrative Film: Film as an Emotion Machine. Routledge.Google ScholarGoogle Scholar
  169. Mariët Theune, Sander Faas, Anton Nijholt, and Dirk Heylen. 2003. The virtual storyteller: Story creation by intelligent agents. In Proceedings of the Technologies for Interactive Digital Storytelling and Entertainment Conference. 204--215.Google ScholarGoogle Scholar
  170. Perry Thorndyke. 1977. Cognitive structures in comprehension and memory of narrative discourse. Cognitive Psychology 9, 1 (1977), 77--110.Google ScholarGoogle ScholarCross RefCross Ref
  171. Van-Khanh Tran and Minh Le Nguyen. 2017. Natural language generation for spoken dialogue system using RNN encoder-decoder networks. In Proceedings of the 21st Conference on Computational Natural Language Learning. 442--451.Google ScholarGoogle ScholarCross RefCross Ref
  172. Scott Turner. 2014. The Creative Process: A Computer Model of Storytelling and Creativity. Psychology Press.Google ScholarGoogle Scholar
  173. Josep Valls-Vargas. 2013. Narrative extraction, processing and generation for interactive fiction and computer games. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, Vol. 9.Google ScholarGoogle Scholar
  174. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. 5998--6008.Google ScholarGoogle Scholar
  175. Bingning Wang, Kang Liu, and Jun Zhao. 2017. Conditional generative adversarial networks for commonsense machine comprehension. In Proceedings of 26th International Joint Conference on Artificial Intelligence. 4123--4129.Google ScholarGoogle ScholarCross RefCross Ref
  176. Su Wang, Greg Durrett, and Katrin Erk. 2020. Narrative interpolation for generating and understanding stories. arXiv:2008.07466Google ScholarGoogle Scholar
  177. Tong Wang, Ping Chen, and Boyang Li. 2017. Predicting the quality of short narratives from social media. In Proceedings of the International Joint Conference on Artificial Intelligence.Google ScholarGoogle ScholarCross RefCross Ref
  178. Tianming Wang and Xiaojun Wan. 2019. T-CVAE: Transformer-based conditioned variational autoencoder for story completion. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 5233--5239.Google ScholarGoogle ScholarCross RefCross Ref
  179. Stephen Ware and R. Michael Young. 2011. CPOCL: A narrative planner supporting conflict. In Proceedings of the 7th Artificial Intelligence and Interactive Digital Entertainment Conference.Google ScholarGoogle Scholar
  180. Peter Weyhrauch. 1997. Guiding Interactive Fiction. Ph.D. Dissertation. Carnegie Mellon University.Google ScholarGoogle Scholar
  181. Robert Wilensky. 1983. Story grammars versus story points. Behavioral and Brain Sciences 6, 4 (1983), 579--591.Google ScholarGoogle ScholarCross RefCross Ref
  182. David Winer, Adam Amos-Binks, Camille Barot, and R. Michael Young. 2015. Good timing for computational models of narrative discourse. In Proceedings of the 6th Workshop on Computational Models of Narrative. 152--156.Google ScholarGoogle Scholar
  183. Jingjing Xu, Xuancheng Ren, Yi Zhang, Qi Zeng, Xiaoyan Cai, and Xu Sun. 2018. A skeleton-based model for promoting coherence among sentences in narrative story generation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 4306--4315.Google ScholarGoogle ScholarCross RefCross Ref
  184. Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Raul Puri, Pascale Fung, Anima Anandkumar, and Bryan Catanzaro. 2020. MEGATRON-CNTRL: Controllable story generation with external knowledge using large-scale language models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2831--2845.Google ScholarGoogle ScholarCross RefCross Ref
  185. Weilai Xu, Charlie Hargood, Wen Tang, and Fred Charles. 2018. Towards generating stylistic dialogues for narratives using data-driven approaches. In Proceedings of the International Conference on Interactive Digital Storytelling. 462--472.Google ScholarGoogle ScholarDigital LibraryDigital Library
  186. Lili Yao, Nanyun Peng, Ralph Weischedel, Kevin Knight, Dongyan Zhao, and Rui Yan. 2019. Plan-and-write: Towards better automatic storytelling. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence. 7378--7385.Google ScholarGoogle ScholarDigital LibraryDigital Library
  187. R. Michael Young, Stephen Ware, Brad Cassell, and Justus Robertson. 2013. Plans and planning in narrative generation: A review of plan-based approaches to the generation of story, discourse and interactivity in narratives. Sprache und Datenverarbeitung 37, 1--2 (2013), 41--64.Google ScholarGoogle Scholar
  188. Fangzhou Zhai, Vera Demberg, Pavel Shkadzko, Wei Shi, and Asad Sayeed. 2019. A hybrid model for globally coherent story generation. In Proceedings of the 2nd Workshop on Storytelling. 34--45.Google ScholarGoogle ScholarCross RefCross Ref
  189. Yan Zhao, Lu Liu, Chunhua Liu, Ruoyao Yang, and Dong Yu. 2018. From plots to endings: A reinforced pointer generator for story ending generation. In Proceedings of the CCF International Conference on Natural Language Processing and Chinese Computing. 51--63.Google ScholarGoogle ScholarDigital LibraryDigital Library
  190. Chenguang Zhu, Michael Zeng, and Xuedong Huang. 2019. Multi-task learning for natural language generation in task-oriented dialogue. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 1261--1266.Google ScholarGoogle ScholarCross RefCross Ref
  191. Jichen Zhu and Santiago Ontañón. 2014. Shall I compare thee to another story?—An empirical study of analogy-based story generation. IEEE Transactions on Computational Intelligence and AI in Games 6, 2 (2014), 216--227.Google ScholarGoogle ScholarCross RefCross Ref
  192. Dolf Zillmann. 1996. The psychology of suspense in dramatic exposition. Suspense: Conceptualizations, Theoretical Analyses, and Empirical Explorations, P. Vorderer, H. J. Wulff, and M. Friedrichsen (Eds.). LEA’s Communication Series. Lawrence Erlbaum Associates, 199--231.Google ScholarGoogle Scholar

Index Terms

  1. Automatic Story Generation: A Survey of Approaches

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in

          Full Access

          • Published in

            cover image ACM Computing Surveys
            ACM Computing Surveys  Volume 54, Issue 5
            June 2022
            719 pages
            ISSN:0360-0300
            EISSN:1557-7341
            DOI:10.1145/3467690
            Issue’s Table of Contents

            Copyright © 2021 ACM

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 25 May 2021
            • Accepted: 1 February 2021
            • Revised: 1 January 2021
            • Received: 1 April 2020
            Published in csur Volume 54, Issue 5

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article
            • Research
            • Refereed

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader

          HTML Format

          View this article in HTML Format .

          View HTML Format