Plot extraction and the visualization of narrative flow

Author:

DeBuse Michael A.ORCID,Warnick Sean

Abstract

Abstract This article discusses the development of an automated plot extraction system for narrative texts. Acknowledging the distinction between plot, as an object of study with its own rich history and literature, and features of a text that may be automatically extractable, we begin by characterizing a text’s scatter plot of entities. This visualization of a text reveals entity density patterns characterizing the particular telling of the story under investigation and leads to effective scene partitioning. We then introduce the concept of narrative flow, a graph representation of the narrative ordering of scenes (the syuzhet) that includes how entities move through scenes from the text, and investigate the degree to which narrative flow can be automatically extracted given a glossary of plot-important objects, actors, and locations. Our subsequent analysis then explores the correlation between subjective notions of plot and the information extracted through these visualizations. In particular, we discuss narrative structures commonly found within the graphs and make comparisons with ground truth narrative flow graphs, showing mixed results highlighting the difficulty of plot extraction. However, the visual artifacts and common structural relationships seen in the graphs provide insight into narrative and its underlying plot.

Publisher

Cambridge University Press (CUP)

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics,Software

Reference85 articles.

1. Reiter, N. (2014). Discovering Structural Similarities in Narrative Texts Using Event Alignment Algorithms. PhD thesis, Ruprecht Karl University of Heidelberg.

2. Naughton, M. , Kushmerick, N. and Carthy, J. (2006). Event extraction from heterogeneous news sources. In Proceedings of the AAAI Workshop Event Extraction and Synthesis, pp. 1–6.

3. Guo, J. , Lu, S. , Cai, H. , Zhang, W. , Yu, Y. and Wang, J. (2017). Long text generation via adversarial training with leaked information. In Proceedings of the 34th International Conference on Machine Learning, 70, pp. 4006–4015.

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