Research and Development

Machine Learning and Narrative


Felt: A Simple Story Sifter by Max Kreminski, Melanie Dickinson and Noah Wardrip-Fruin, Toward Example-driven Program Synthesis of Story Sifting Patterns by Max Kreminski, Noah Wardrip-Fruin and Michael Mateas and Winnow: A Domain-specific Language for Incremental Story Sifting by Max Kreminski, Melanie Dickinson and Michael Mateas.


Automatic Text Summarization Using a Machine Learning Approach by Joel L. Neto, Alex A. Freitas and Celso A. A. Kaestner, Summarizing Narratives by Wendy G. Lehnert, John B. Black and Brian J. Reiser, Summarizing Stories after Reading and Listening by Walter Kintsch and Ely Kozminsky and Narrative Complexity Based on Summarization Algorithms by Wendy G. Lehnert.


Towards Automatic Story Clustering for Interactive Narrative Authoring by Michal Bída, Martin Černý and Cyril Brom and Follow-up on Automatic Story Clustering for Interactive Narrative Authoring by Michal Bída, Martin Černý and Cyril Brom.


InspireMe: Learning Sequence Models for Stories by Vincent Fortuin, Romann Weber, Sasha Schriber, Diana Wotruba and Markus Gross, An Exploration of Automated Narrative Analysis via Machine Learning by Sharad Jones, Carly Fox, Sandra Gillam and Ronald B. Gillam, Strategic Production of Predictive Inferences during Comprehension by David Allbritton, The Role of Predictive Inferences in Situation Model Construction by Rebecca Fincher‐Kiefer, Narrative Progression in the Short Story: A Corpus Stylistic Approach by Michael J. Toolan, Integrating External Event Knowledge for Script Learning by Shangwen Lv, Fuqing Zhu and Songlin Hu, Constructing Narrative Event Evolutionary Graph for Script Event Prediction by Zhongyang Li, Xiao Ding and Ting Liu, Story Ending Prediction by Transferable BERT by Zhongyang Li, Xiao Ding and Ting Liu, Semantic Frame Forecast by Chieh-Yang Huang and Ting-Hao Huang, Learning the Predictability of the Future by Dídac Surís, Ruoshi Liu and Carl Vondrick and Story Comprehension for Predicting What Happens Next by Snigdha Chaturvedi, Haoruo Peng and Dan Roth.

Computational Literary Aesthetics

The Poetics, Aesthetics, and Philosophy of Narrative by Noël Carroll, Literary Aesthetics: A Reader by Alan Singer and Allen Dunn, Aesthetics and Literature by David Davies, The Aesthetics of Mimesis by Stephen Halliwell, Film Art: An Introduction by David Bordwell, Kristin Thompson and Jeff Smith, Aesthetics and Film by Katherine Thomson-Jones, The Aesthetics and Psychology of the Cinema by Jean Mitry and Christopher King, Evaluating Computational Creativity: An Interdisciplinary Tutorial by Carolyn Lamb, Daniel G. Brown and Charles L. A. Clarke and Computational Aesthetic Evaluation: Past and Future by Philip Galanter.

Audience Response Prediction

Aesthetic Responses to the Characters, Plots, Worlds, and Style of Stories by Marta M. Maslej, Joshua A. Quinlan and Raymond A. Mar and How Quantifying the Shape of Stories Predicts their Success by Olivier Toubia, Jonah Berger and Jehoshua Eliashberg.


Towards Learning from Stories: An Approach to Interactive Machine Learning by Brent Harrison and Mark O. Riedl and Learning from Stories: Using Natural Communication to Train Believable Agents by Brent Harrison, Siddhartha Banerjee and Mark O. Riedl.