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Automatic Story Generation: Challenges and Attempts

Summary

This survey addresses automatic story generation. Even modern transformer models struggle with this task: they have difficulty maintaining a coherent plot, consistent characters, and genre conventions. The authors, from Georgia Tech, do not propose a new model or dataset. Instead, they organize the existing literature differently from previous surveys: not by architecture type but by the field's key challenges. There are four such challenges: controllability of generation, use of commonsense knowledge, plausible character actions, and creativity of language. For each challenge, the authors examine specific systems, their approaches, strengths, and weaknesses. The main conclusions are as follows. The more tightly generation is controlled, the duller the stories become. Standard NLG metrics are poor at assessing narrative quality. Illogical and inconsistent plots remain an open problem. The paper is useful to newcomers as an initial map of the field. It also summarizes the state of the art at the threshold of the shift from seq2seq to large language models.

Key points

  • Controllability: the survey covers RL with reward shaping (Tambwekar et al., 2019), which has to be retrained for each new goal, and the Fusion model (Fan et al., 2018) on WRITING PROMPTS with a "premise → story" scheme. It also covers Plan-and-write (Yao et al., 2019), which uses a "title → storyline → text" scheme and inter- and intra-story repetition metrics. Plot Machines (Rashkin et al., 2020) combine an outline, memory, and discourse labels (introduction/body/conclusion). Generation by interpolation (Wang et al., 2020) generates candidates with GPT-2 and ranks them with RoBERTa by their consistency with the first and last sentences.
  • Trend: each of the reviewed works controls either the overall plan of the story or its ending. Overall, the field is shifting noticeably toward transformer models.
  • Commonsense: the most common approaches are fine-tuning on commonsense data, causality analysis, and incorporating knowledge graphs into encoders. All of them improve quality, but none guarantees a logical story.
  • Characters: an abstract representation of events as "predicate + arguments", with separate attention to preceding verbs, produced more coherent texts and more diverse verbs than the Fusion model. EC-CLF and EC-EM (Brahman & Chaturvedi, 2020) use RL to make GPT-2 follow the protagonist's emotional arc, which is evaluated with COMET. Interaction among multiple characters has barely been studied.
  • Future directions: metrics designed specifically for narratives (creativity, interestingness) are needed in place of BLEU, ROUGE, and perplexity. The authors also recommend drawing commonsense inferences at the level of the whole story rather than individual sentences or paragraphs, and seeking a balance between creativity and structural coherence.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.nuse-1.8 · arXiv 2102.12634

@inproceedings{alabdulkarim2021automatic,
  title = {{Automatic Story Generation: Challenges and Attempts}},
  author = {Alabdulkarim, Amal and Li, Siyan and Peng, Xiangyu},
  booktitle = {Proceedings of the Third Workshop on Narrative Understanding},
  year = {2021},
  doi = {10.18653/v1/2021.nuse-1.8},
  eprint = {2102.12634},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2021.nuse-1.8}
}

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