# Detecting Narrative Elements in Informational Text

arXiv: https://arxiv.org/abs/2210.03028 · HTML: https://mlsanity.com/paper/2210.03028/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 48 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Movie Plot Analysis via Turning Point Identification](https://mlsanity.com/paper/1908.10328/) (2019)
- [Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types](https://mlsanity.com/paper/2005.12762/) (2020)

## Built on by (most cited first, 4 of 4)

- [QUDsim: Quantifying Discourse Similarities in LLM-Generated Text](https://mlsanity.com/paper/2504.09373/) (2025)
- [Beyond LLMs: A Linguistic Approach to Causal Graph Generation from Narrative Texts](https://mlsanity.com/paper/2504.07459/) (2025)
- [Reap the Wild Wind: Detecting Media Storms in Large-Scale News Corpora](https://mlsanity.com/paper/2404.09299/) (2024)
- [Developing a Guideline for the Labovian-Structural Analysis of Oral Narratives in Japanese](https://mlsanity.com/paper/2603.29347/) (2026)

## Abstract (arXiv)

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.