# Salience-Aware Event Chain Modeling for Narrative Understanding

arXiv: https://arxiv.org/abs/2109.10475 · HTML: https://mlsanity.com/paper/2109.10475/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 44 references that are in the map.
- First posted on arXiv 22 Sep 2021.

## Builds on (most cited first, 21 of 21)

- [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)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [Plan-And-Write: Towards Better Automatic Storytelling](https://mlsanity.com/paper/1811.05701/) (2018)
- [TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions](https://mlsanity.com/paper/2005.00242/) (2020)
- [Temporal Reasoning on Implicit Events from Distant Supervision](https://mlsanity.com/paper/2010.12753/) (2020)
- [Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction](https://mlsanity.com/paper/1909.05360/) (2019)
- [Joint Constrained Learning for Event-Event Relation Extraction](https://mlsanity.com/paper/2010.06727/) (2020)
- [Goal-Oriented Script Construction](https://mlsanity.com/paper/2107.13189/) (2021)
- [Analogous Process Structure Induction for Sub-event Sequence Prediction](https://mlsanity.com/paper/2010.08525/) (2020)
- [Improving Machine Reading Comprehension with General Reading Strategies](https://mlsanity.com/paper/1810.13441/) (2018)
- [MCScript2.0: A Machine Comprehension Corpus Focused on Script Events and Participants](https://mlsanity.com/paper/1905.09531/) (2019)
- ["What Are You Trying to Do?" Semantic Typing of Event Processes](https://mlsanity.com/paper/2010.06724/) (2020)
- [Automatic Event Salience Identification](https://mlsanity.com/paper/1809.00647/) (2018)
- [Event Representations with Tensor-based Compositions](https://mlsanity.com/paper/1711.07611/) (2017)
- [Conditional Generation of Temporally-ordered Event Sequences](https://mlsanity.com/paper/2012.15786/) (2020)
- [A Simple and Effective Approach to the Story Cloze Test](https://mlsanity.com/paper/1803.05547/) (2018)
- [Hierarchical Quantized Representations for Script Generation](https://mlsanity.com/paper/1808.09542/) (2018)
- [Implicit Argument Prediction with Event Knowledge](https://mlsanity.com/paper/1802.07226/) (2018)

## Built on by (most cited first, 15 of 15)

- [Identifying Conspiracy Theories News based on Event Relation Graph](https://mlsanity.com/paper/2310.18545/) (2023)
- [CoRRPUS: Code-based Structured Prompting for Neurosymbolic Story Understanding](https://mlsanity.com/paper/2212.10754/) (2022)
- [NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization](https://mlsanity.com/paper/2212.01476/) (2022)
- [EMONA: Event-level Moral Opinions in News Articles](https://mlsanity.com/paper/2404.01715/) (2024)
- [Media Framing through the Lens of Event-Centric Narratives](https://mlsanity.com/paper/2410.03151/) (2024)
- [Schema-Guided Event Graph Completion](https://mlsanity.com/paper/2206.02921/) (2022)
- [Cascading Large Language Models for Salient Event Graph Generation](https://mlsanity.com/paper/2406.18449/) (2024)
- [A Multi-Level Benchmark for Causal Language Understanding in Social Media Discourse](https://mlsanity.com/paper/2509.16722/) (2025)
- [NECE: Narrative Event Chain Extraction Toolkit](https://mlsanity.com/paper/2208.08063/) (2022)
- [Multi-document Summarization through Multi-document Event Relation Graph Reasoning in LLMs: a case study in Framing Bias Mitigation](https://mlsanity.com/paper/2506.12978/) (2025)
- [Contrastive Learning with Narrative Twins for Modeling Story Salience](https://mlsanity.com/paper/2601.07765/) (2026)
- [News is More than a Collection of Facts: Moral Frame Preserving News Summarization](https://mlsanity.com/paper/2504.00657/) (2025)
- [Supernova Event Dataset: Interpreting Large Language Models' Personality through Critical Event Analysis](https://mlsanity.com/paper/2506.12189/) (2025)
- [Story Ribbons: Reimagining Storyline Visualizations with Large Language Models](https://mlsanity.com/paper/2508.06772/) (2025)
- [Narrative Lookahead in LLMs: Event-Level Decodability, Predictive Advantage, and Steering](https://mlsanity.com/paper/2610.18121/) (2026)

## Abstract (arXiv)

Storytelling, whether via fables, news reports, documentaries, or memoirs, can be thought of as the communication of interesting and related events that, taken together, form a concrete process. It is desirable to extract the event chains that represent such processes. However, this extraction remains a challenging problem. We posit that this is due to the nature of the texts from which chains are discovered. Natural language text interleaves a narrative of concrete, salient events with background information, contextualization, opinion, and other elements that are important for a variety of necessary discourse and pragmatics acts but are not part of the principal chain of events being communicated. We introduce methods for extracting this principal chain from natural language text, by filtering away non-salient events and supportive sentences. We demonstrate the effectiveness of our methods at isolating critical event chains by comparing their effect on downstream tasks. We show that by pre-training large language models on our extracted chains, we obtain improvements in two tasks that benefit from a clear understanding of event chains: narrative prediction and event-based temporal question answering. The demonstrated improvements and ablative studies confirm that our extraction method isolates critical event chains.