# M-SENSE: Modeling Narrative Structure in Short Personal Narratives Using Protagonist's Mental Representations

arXiv: https://arxiv.org/abs/2302.09418 · HTML: https://mlsanity.com/paper/2302.09418/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 71 references that are in the map.
- First posted on arXiv 18 Feb 2023.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [The NarrativeQA Reading Comprehension Challenge](https://mlsanity.com/paper/1712.07040/) (2017)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Narrative Planning: Balancing Plot and Character](https://mlsanity.com/paper/1401.3841/) (2014)
- [Modeling Naive Psychology of Characters in Simple Commonsense Stories](https://mlsanity.com/paper/1805.06533/) (2018)
- [Movie Plot Analysis via Turning Point Identification](https://mlsanity.com/paper/1908.10328/) (2019)
- [Machine Comprehension by Text-to-Text Neural Question Generation](https://mlsanity.com/paper/1705.02012/) (2017)
- [MMFT-BERT: Multimodal Fusion Transformer with BERT Encodings for Visual Question Answering](https://mlsanity.com/paper/2010.14095/) (2020)
- [Supervised and Unsupervised Transfer Learning for Question Answering](https://mlsanity.com/paper/1711.05345/) (2017)
- [Incorporating Structured Commonsense Knowledge in Story Completion](https://mlsanity.com/paper/1811.00625/) (2018)
- [Screenplay Summarization Using Latent Narrative Structure](https://mlsanity.com/paper/2004.12727/) (2020)
- [From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels and Fairy Tales](https://mlsanity.com/paper/1309.5909/) (2013)
- [CompRes: A Dataset for Narrative Structure in News](https://mlsanity.com/paper/2007.04874/) (2020)

## Built on by (most cited first, 3 of 3)

- [Affective and Dynamic Beam Search for Story Generation](https://mlsanity.com/paper/2310.15079/) (2023)
- [Self-Regulated Data-Free Knowledge Amalgamation for Text Classification](https://mlsanity.com/paper/2406.15476/) (2024)
- [LitVISTA: A Benchmark for Narrative Orchestration in Literary Text](https://mlsanity.com/paper/2601.06445/) (2026)

## Abstract (arXiv)

Narrative is a ubiquitous component of human communication. Understanding its structure plays a critical role in a wide variety of applications, ranging from simple comparative analyses to enhanced narrative retrieval, comprehension, or reasoning capabilities. Prior research in narratology has highlighted the importance of studying the links between cognitive and linguistic aspects of narratives for effective comprehension. This interdependence is related to the textual semantics and mental language in narratives, referring to characters' motivations, feelings or emotions, and beliefs. However, this interdependence is hardly explored for modeling narratives. In this work, we propose the task of automatically detecting prominent elements of the narrative structure by analyzing the role of characters' inferred mental state along with linguistic information at the syntactic and semantic levels. We introduce a STORIES dataset of short personal narratives containing manual annotations of key elements of narrative structure, specifically climax and resolution. To this end, we implement a computational model that leverages the protagonist's mental state information obtained from a pre-trained model trained on social commonsense knowledge and integrates their representations with contextual semantic embed-dings using a multi-feature fusion approach. Evaluating against prior zero-shot and supervised baselines, we find that our model is able to achieve significant improvements in the task of identifying climax and resolution.