A model of suspense for narrative generation

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 on the automatic learning of narrative schemas suggests an alternative approach that exploits such schemas 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.

[1]  Roger C. Schank,et al.  Scripts, plans, goals and understanding: an inquiry into human knowledge structures , 1978 .

[2]  Gonzalo Méndez,et al.  A Challenge Proposal for Narrative Generation Using CNLs , 2016, INLG.

[3]  S. Turner.  Minstrel: a computer model of creativity and storytelling , 1993 .

[4]  Robert Michael Young,et al.  Narrative Planning: Balancing Plot and Character , 2010, J. Artif. Intell. Res..

[5]  Brian O'Neill,et al.  Dramatis: A Computational Model of Suspense , 2014, AAAI.

[6]  Hans Hoeken,et al.  Suspense, curiosity, and surprise: How discourse structure influences the affective and cognitive processing of a story , 2000 .

[7]  Rolf A. Zwaan,et al.  The Construction of Situation Models in Narrative Comprehension: An Event-Indexing Model , 1995 .

[8]  Robert Michael Young,et al.  Suspenser: A Story Generation System for Suspense , 2015, IEEE Transactions on Computational Intelligence and AI in Games.

[9]  Stephen G. Ware,et al.  Glaive: A State-Space Narrative Planner Supporting Intentionality and Conflict , 2014, AIIDE.

[10]  James C. Lester,et al.  Narrative prose generation , 2001, Artif. Intell..

[11]  R. A. Doust,et al.  A domain-independent model of suspense in narrative , 2015 .

[12]  R. Gerrig,et al.  Readers as problem-solvers in the experience of suspense , 1994 .

[13]  Nathanael Chambers,et al.  A Database of Narrative Schemas , 2010, LREC.

[14]  Marc Cavazza,et al.  Dialogue Generation in Character-based Interactive Storytelling , 2005, AIIDE.

[15]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[16]  Michael Lebowitz,et al.  Story-telling as planning and learning , 1985 .

[17]  Nathanael Chambers,et al.  Unsupervised Learning of Narrative Schemas and their Participants , 2009, ACL.

[18]  Angelo E. M. Ciarlini,et al.  The Usage of the Structural-Affect Theory of Stories for Narrative Generation , 2011, 2011 Brazilian Symposium on Games and Digital Entertainment.

[19]  Marc Cavazza,et al.  Character-Based Interactive Storytelling , 2002, IEEE Intell. Syst..

[20]  T. Trabasso,et al.  Constructing inferences during narrative text comprehension. , 1994, Psychological review.

[21]  Pablo Gervás,et al.  A Component-Based Architecture for Suspense Modelling , 2016 .

[22]  William F. Brewer,et al.  Stories Are to Entertain: A Structural-Affect Theory of Stories. Technical Report No. 265. , 1982 .

[23]  James R. Meehan,et al.  TALE-SPIN, An Interactive Program that Writes Stories , 1977, IJCAI.

[24]  D. Zillmann.  The Psychology of Suspense in Dramatic Exposition , 2013 .

[25]  W. Kintsch.  Learning from text, levels of comprehension, or: Why anyone would read a story anyway , 1980 .

[26]  A. Sorace,et al.  MAGNITUDE ESTIMATION OF LINGUISTIC ACCEPTABILITY , 1996 .

[27]  W. T. Maddox,et al.  Recency effects as a window to generalization: separating decisional and perceptual sequential effects in category learning. , 2006, Journal of experimental psychology. Learning, memory, and cognition.

[28]  Rafael Pérez y Pérez,et al.  MEXICA: A computer model of a cognitive account of creative writing , 2001, J. Exp. Theor. Artif. Intell..