Detecting Narrative Elements in Informational Text
This work addresses the challenge of narrative analysis in news media, which is incremental as it adapts existing theories to a new text type.
The paper tackled the problem of detecting narrative elements in informational text, specifically news stories, by introducing a new NLP task called NEAT and creating a multi-label annotation scheme adapted from narrative theory, achieving an average F1 score of up to 0.77 on a dataset of 2,209 sentences.
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.