@article {770,
	title = {Predicting the effects of suspenseful outcome for automatic storytelling},
	journal = {Knowledge-Based Systems},
	volume = {209},
	year = {2020},
	pages = {106450},
	abstract = {Automatic story generation systems usually deliver suspense by including an adverse outcome in the narrative, in the assumption that the adversity will trigger a certain set of emotions that can be categorized as suspenseful. However, existing systems do not implement solutions relying on predictive models of the impact of the outcome on readers. A formulation of the emotional effects of the outcome would allow storytelling systems to perform a better measure of suspense and discriminate among potential outcomes based on the emotional impact. This paper reports on a computational model of the effect of different outcomes on the perceived suspense. A preliminary analysis to identify and evaluate the affective responses to a set of outcomes commonly used in suspense was carried out. Then, a study was run to quantify and compare suspense and affective responses evoked by the set of outcomes. Next, a predictive model relying on the analyzed data was computed, and an evolutionary algorithm for automatically choosing the best outcome was implemented. The system was tested against human subjects{\textquoteright} reported suspense and electromyography responses to the addition of the generated outcomes to narrative passages. The results show a high correlation between the predicted impact of the computed outcome and the reported suspense.},
	doi = {https://doi.org/10.1016/j.knosys.2020.106450},
	url = {https://www.sciencedirect.com/science/article/pii/S0950705120305797},
	author = {Delatorre, Pablo and Le{\'o}n, Carlos and Salguero, Alberto and Tapscott, Alan}
}
