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International Conference on Smart Computing and Communication

SmartCom 2019: Smart Computing and Communication pp 1–10Cite as

A Survey of Deep Learning Applied to Story Generation

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11910))

  • The original version of this chapter was revised: the last name of the author Sisi Xuanyuan had been misspelt misspelt. This has been corrected. The correction to this chapter is available at https://doi.org/10.1007/978-3-030-34139-8_41

Abstract

With the rapid development of deep learning in the fields of text abstraction and dialogue generation, researchers are now reconsidering the long-standing story-generation task from the 1970s. Deep learning methods are gradually being adopted to solve problems in traditional story generation, making story generation a new research hotspot in the field of text generation. However, in the field of story generation, the widely used seq2seq model is unable to provide adequate long-distance text modeling. As a result, this model struggles to solve the story-generation task, since the relation between long text should be considered, and coherency and vividness are critical. Thus, recent years have seen numerous proposals for better modeling methods. In this paper, we present the results of a comprehensive study of story generation. We first introduce the relevant concepts of story generation, its background, and the current state of research. We then summarize and analyze the standard methods of story generation. Based on various divisions of user constraints, the story-generation methods are divided into three categories: the theme-oriented model, the storyline-oriented model, and the human-machine-interaction–oriented model. On this basis, we discuss the basic ideas and main concerns of various methods and compare the strengths and weaknesses of each method. Finally, we finish by analyzing and forecasting future developments that could push story-generation research toward a new frontier.

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Change history

  • 04 November 2019

    In the version of this paper that was originally published, the last name of the author Sisi Xuanyuan was misspelt. This has been corrected.

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Acknowledgment

This work is supported by the National Key Research and Development Program of China (No. 2017YFB1400805).

Author information

Authors and Affiliations

  1. National Engineering Research Center for Software Engineering, Peking University, Beijing, 100871, China

    Chenglong Hou & Jinan Sun

  2. School of Software and Microelectronics, Peking University, Beijing, 100871, China

    Chenglong Hou & Kun Zhou

  3. Potevio Information Technology Co., Ltd., Beijing, China

    Chensong Zhou

  4. Department of Science and Technology of Shandong Province, Jinan, China

    Sisi Xuanyuan

Authors
  1. Chenglong Hou
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  2. Chensong Zhou
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  3. Kun Zhou
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  4. Jinan Sun
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  5. Sisi Xuanyuan
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Corresponding author

Correspondence to Jinan Sun .

Editor information

Editors and Affiliations

  1. Columbia University, New York, NY, USA

    Meikang Qiu

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Hou, C., Zhou, C., Zhou, K., Sun, J., Xuanyuan, S. (2019). A Survey of Deep Learning Applied to Story Generation. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2019. Lecture Notes in Computer Science(), vol 11910. Springer, Cham. https://doi.org/10.1007/978-3-030-34139-8_1

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  • DOI: https://doi.org/10.1007/978-3-030-34139-8_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34138-1

  • Online ISBN: 978-3-030-34139-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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