# Towards a Formal Model of Narratives

arXiv: https://arxiv.org/abs/2103.12872 · HTML: https://mlsanity.com/paper/2103.12872/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 23 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation](https://mlsanity.com/paper/1703.09902/) (2017)
- [Event Representations for Automated Story Generation with Deep Neural Nets](https://mlsanity.com/paper/1706.01331/) (2017)
- [Progressive Attention Memory Network for Movie Story Question Answering](https://mlsanity.com/paper/1904.08607/) (2019)

## Built on by (most cited first, 6 of 6)

- [Enhancing Presentation Slide Generation by LLMs with a Multi-Staged End-to-End Approach](https://mlsanity.com/paper/2406.06556/) (2024)
- [Cut the CARP: Fishing for zero-shot story evaluation](https://mlsanity.com/paper/2110.03111/) (2021)
- [Media Framing through the Lens of Event-Centric Narratives](https://mlsanity.com/paper/2410.03151/) (2024)
- [Topical Segmentation of Spoken Narratives: A Test Case on Holocaust Survivor Testimonies](https://mlsanity.com/paper/2210.13783/) (2022)
- [Identifying Narrative Patterns and Outliers in Holocaust Testimonies Using Topic Modeling](https://mlsanity.com/paper/2405.02650/) (2024)
- [Reconstructing Persistent Worlds from Narratives for Narrative-Grounded Interactive Experiences](https://mlsanity.com/paper/2608.04037/) (2026)

## Abstract (arXiv)

In this paper, we propose the beginnings of a formal framework for modeling narrative textitqua narrative. Our framework affords the ability to discuss key qualities of stories and their communication, including the flow of information from a Narrator to a Reader, the evolution of a Reader's story model over time, and Reader uncertainty. We demonstrate its applicability to computational narratology by giving explicit algorithms for measuring the accuracy with which information was conveyed to the Reader and two novel measurements of story coherence.