  Automatic extraction of narrative elements from text, combining narrative
theories with computational models, has been receiving increasing attention
over the last few years. Previous works have utilized the oral narrative theory
by Labov and Waletzky to identify various narrative elements in personal
stories texts. Instead, we direct our focus to informational texts,
specifically news stories. We introduce NEAT (Narrative Elements AnnoTation) -
a novel NLP task for detecting narrative elements in raw text. For this
purpose, we designed a new multi-label narrative annotation scheme, better
suited for informational text (e.g. news media), by adapting elements from the
narrative theory of Labov and Waletzky (Complication and Resolution) and adding
a new narrative element of our own (Success). We then used this scheme to
annotate a new dataset of 2,209 sentences, compiled from 46 news articles from
various category domains. We trained a number of supervised models in several
different setups over the annotated dataset to identify the different narrative
elements, achieving an average F1 score of up to 0.77. The results demonstrate
the holistic nature of our annotation scheme as well as its robustness to
domain category.
