Abstract
Most work on automatic generation of narratives, and more specifically suspenseful narrative, has focused on detailed domain-specific modelling of character psychology and plot structure. Recent work in computational linguistics on the automatic learning of narrative schemas suggests an alternative approach that exploits such schemas as a starting point for modelling and measuring suspense. We propose a domain-independent model for tracking suspense in a story which can be used to predict the audience’s suspense response on a sentence-by-sentence basis at the content determination stage of narrative generation. The model lends itself as the theoretical foundation for a suspense module that is compatible with alternative narrative generation theories. The proposal is evaluated by human judges’ normalised average scores correlate strongly with predicted values.- Anthology ID:
- W17-3527
- Volume:
- Proceedings of the 10th International Conference on Natural Language Generation
- Month:
- September
- Year:
- 2017
- Address:
- Santiago de Compostela, Spain
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 178–187
- Language:
- URL:
- https://aclanthology.org/W17-3527
- DOI:
- 10.18653/v1/W17-3527
- Cite (ACL):
- Richard Doust and Paul Piwek. 2017. A model of suspense for narrative generation. In Proceedings of the 10th International Conference on Natural Language Generation, pages 178–187, Santiago de Compostela, Spain. Association for Computational Linguistics.
- Cite (Informal):
- A model of suspense for narrative generation (Doust & Piwek, INLG 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W17-3527.pdf