# Whats New? Identifying the Unfolding of New Events in Narratives

arXiv: https://arxiv.org/abs/2302.07748 · HTML: https://mlsanity.com/paper/2302.07748/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 15 Feb 2023.

## Builds on (most cited first, 9 of 9)

- [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)
- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- ["Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding](https://mlsanity.com/paper/1909.03065/) (2019)
- [Event2Mind: Commonsense Inference on Events, Intents, and Reactions](https://mlsanity.com/paper/1805.06939/) (2018)
- [Multi-Sentence Argument Linking](https://mlsanity.com/paper/1911.03766/) (2019)
- [Modeling emotion in complex stories: the Stanford Emotional Narratives Dataset](https://mlsanity.com/paper/1912.05008/) (2019)
- [A Survey of Machine Narrative Reading Comprehension Assessments](https://mlsanity.com/paper/2205.00299/) (2022)
- [Annotation of Emotion Carriers in Personal Narratives](https://mlsanity.com/paper/2002.12196/) (2020)

## Built on by (most cited first, 1 of 1)

- [Will LLMs Replace the Encoder-Only Models in Temporal Relation Classification?](https://mlsanity.com/paper/2410.10476/) (2024)

## Abstract (arXiv)

Narratives include a rich source of events unfolding over time and context. Automatic understanding of these events provides a summarised comprehension of the narrative for further computation (such as reasoning). In this paper, we study the Information Status (IS) of the events and propose a novel challenging task: the automatic identification of new events in a narrative. We define an event as a triplet of subject, predicate, and object. The event is categorized as new with respect to the discourse context and whether it can be inferred through commonsense reasoning. We annotated a publicly available corpus of narratives with the new events at sentence level using human annotators. We present the annotation protocol and study the quality of the annotation and the difficulty of the task. We publish the annotated dataset, annotation materials, and machine learning baseline models for the task of new event extraction for narrative understanding.