Modeling Reportable Events as Turning Points in Narrative

Abstract

We present novel experiments in modeling the rise and fall of story characteristics within narrative, leading up to the Most Reportable Event (MRE), the compelling event that is the nucleus of the story. We construct a corpus of personal narratives from the bulletin board website Reddit, using the organization of Reddit content into topic-specific communities to automatically identify narratives. Leveraging the structure of Reddit comment threads, we automatically label a large dataset of narratives. We present a change-based model of narrative that tracks changes in formality, affect, and other characteristics over the course of a story, and we use this model in distant supervision and selftraining experiments that achieve significant improvements over the baselines at the task of identifying MREs.

Modeling Naive Psychology of Characters in Simple Commonsense Stories

“…This poses several unique challenges as annotations require (1) interpreting discourse (2) understanding implicit causal effects, and (3) understanding formal psychology theory categories. In prior literature, annotations of this complexity have typically been performed by experts (Deng and Wiebe, 2015;Ouyang and McKeown, 2015). While reliable, these annotations are prohibitively expensive to scale up.…”

Learning to Listen: Critically Considering the Role of AI in Human Storytelling and Character Creation

“…In addition, some research in modeling stories has focused not on story generation, but on understanding (and subsequently improving) human experiences of narratives. For example, work in identifying "emotional arcs" looks at mood shifts and audience engagement in experiencing narratives (Chu and Roy, 2017;Reagan, 2017), and other work has endeavored to identify turning points in stories (Ouyang and McKeown, 2015), model the shapes of stories (Mani, 2012;Elson, 2012), and understand the relationships of characters to narrative arcs (Bamman et al, 2014b,a). These works have identified what people enjoy about stories, have learned from our existing stories, and subsequently can augment story generation efforts.…”

Crowd-Sourced Iterative Annotation for Narrative Summarization Corpora

“…Gordon and Swanson (2009) trained a classifier to identify narratives in blog posts with 75% precision and built a corpus of 937,994 narratives. Ouyang and McKeown (2015) created a corpus of 4,647 narratives collected automatically from Reddit, achieving 94% precision in collecting only narrative text. They argue that the Most Reportable Event (MRE) is the most salient event and thus the shortest possible summary; they annotated a subset of 476 narratives by extracting sentences that referred to MREs.…”

“…We use the annotated subset of 476 personal narratives in Ouyang and McKeown (2015), although we do not use their annotations.…”