Novels are often adapted into feature films, but the differences between the two media usually require dropping sections of the source text from the movie script.Here we study this screen adaptation process by constructing narrative alignments using the Smith-Waterman local alignment algorithm coupled with SBERT embedding distance to quantify text similarity between scenes and book units.We use these alignments to perform an automated analysis of 40 adaptations, revealing insights into the screenwriting process concerning (i) faithfulness of adaptation, (ii) importance of dialog, (iii) preservation of narrative order, and (iv) gender representation issues reflective of the Bechdel test.

Recognizing the flow of time in a story is a crucial aspect of understanding it.Prior work related to time has primarily focused on identifying temporal expressions or relative sequencing of events, but here we propose computationally annotating each line of a book with wall clock times, even in the absence of explicit time-descriptive phrases.To do so, we construct a data set of hourly time phrases from 52,183 fictional books.We then construct a time-of-day classification model that achieves an average error of 2.27 hours.Furthermore, we show that by analyzing a book in whole using dynamic programming of breakpoints, we can roughly partition a book into segments that each correspond to a particular time-of-day.This approach improves upon baselines by over two hour.Finally, we apply our model to a corpus of literature categorized by different periods in history, to show interesting trends of hourly activity throughout the past.Among several observations we find that the fraction of events taking place past 10 P.M jumps past 1880 -coincident with the advent of the electric light bulb and city lights.

Books are typically segmented into chapters and sections, representing coherent subnarratives and topics.We investigate the task of predicting chapter boundaries, as a proxy for the general task of segmenting long texts.We build a Project Gutenberg chapter segmentation data set of 9,126 English novels, using a hybrid approach combining neural inference and rule matching to recognize chapter title headers in books, achieving an F1-score of 0.77 on this task.Using this annotated data as ground truth after removing structural cues, we present cut-based and neural methods for chapter segmentation, achieving an F1-score of 0.453 on the challenging task of exact break prediction over book-length documents.Finally, we reveal interesting historical trends in the chapter structure of novels.

Charuta Pethe, Steve Skiena. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.