# A Survey on Event-based News Narrative Extraction

arXiv: https://arxiv.org/abs/2302.08351 · HTML: https://mlsanity.com/paper/2302.08351/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 142 references that are in the map.
- First posted on arXiv 16 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)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation](https://mlsanity.com/paper/1703.09902/) (2017)
- [Examining the State-of-the-Art in News Timeline Summarization](https://mlsanity.com/paper/2005.10107/) (2020)
- [The Future of Misinformation Detection: New Perspectives and Trends](https://mlsanity.com/paper/1909.03654/) (2019)
- [Growing Story Forest Online from Massive Breaking News](https://mlsanity.com/paper/1803.00189/) (2018)
- [How to evaluate data visualizations across different levels of understanding](https://mlsanity.com/paper/2009.01747/) (2020)
- [Analyzing Evolving Stories in News Articles](https://mlsanity.com/paper/1703.08593/) (2017)
- [Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events](https://mlsanity.com/paper/1701.03947/) (2017)

## Built on by (most cited first, 16 of 16)

- [ULTRA: Unleash LLMs' Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Self-Refinement](https://mlsanity.com/paper/2401.13218/) (2024)
- [TIMELINE: Exhaustive Annotation of Temporal Relations Supporting the Automatic Ordering of Events in News Articles](https://mlsanity.com/paper/2310.17802/) (2023)
- [Unveiling Divergent Inductive Biases of LLMs on Temporal Data](https://mlsanity.com/paper/2404.01453/) (2024)
- [Interactive Narrative Analytics: Bridging Computational Narrative Extraction and Human Sensemaking](https://mlsanity.com/paper/2601.11459/) (2026)
- [DataTales: A Benchmark for Real-World Intelligent Data Narration](https://mlsanity.com/paper/2410.17859/) (2024)
- [Semantic Interaction for Narrative Map Sensemaking: An Insight-based Evaluation](https://mlsanity.com/paper/2603.29651/) (2026)
- [Lost in Recursion: Mining Rich Event Semantics in Knowledge Graphs](https://mlsanity.com/paper/2404.16405/) (2024)
- [Explainable AI Components for Narrative Map Extraction](https://mlsanity.com/paper/2503.16554/) (2025)
- [Agenda-based Narrative Extraction: Steering Pathfinding Algorithms with Large Language Models](https://mlsanity.com/paper/2603.29661/) (2026)
- [Distilling Event Sequence Knowledge From Large Language Models](https://mlsanity.com/paper/2401.07237/) (2024)
- [Evaluating the Ability of Computationally Extracted Narrative Maps to Encode Media Framing](https://mlsanity.com/paper/2405.02677/) (2024)
- [A Directed Graph Model and Experimental Framework for Design and Study of Time-Dependent Text Visualisation](https://mlsanity.com/paper/2603.02422/) (2026)
- [Event-Centric Human Value Understanding in News-Domain Texts: An Actor-Conditioned, Multi-Granularity Benchmark](https://mlsanity.com/paper/2603.17838/) (2026)
- [An Information-Geometric Justification for Composite Coherence in Event-Based Narrative Extraction](https://mlsanity.com/paper/2606.29118/) (2026)
- [Information Terra: A Narrative-Anchored Semantic-First Projection of Document Embeddings](https://mlsanity.com/paper/2606.30824/) (2026)
- [Automated Identification of Competing Narratives in Political Discourse on Social Media](https://mlsanity.com/paper/2609.11202/) (2026)

## Abstract (arXiv)

Narratives are fundamental to our understanding of the world, providing us with a natural structure for knowledge representation over time. Computational narrative extraction is a subfield of artificial intelligence that makes heavy use of information retrieval and natural language processing techniques. Despite the importance of computational narrative extraction, relatively little scholarly work exists on synthesizing previous research and strategizing future research in the area. In particular, this article focuses on extracting news narratives from an event-centric perspective. Extracting narratives from news data has multiple applications in understanding the evolving information landscape. This survey presents an extensive study of research in the area of event-based news narrative extraction. In particular, we screened over 900 articles that yielded 54 relevant articles. These articles are synthesized and organized by representation model, extraction criteria, and evaluation approaches. Based on the reviewed studies, we identify recent trends, open challenges, and potential research lines.