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Agenda-based Narrative Extraction: Steering Pathfinding Algorithms with Large Language Models

Brian Felipe Keith-Norambuena, Carolina Inés Rojas-Córdova, Claudio Juvenal Meneses-Villegas, Elizabeth Johanna Lam-Esquenazi, Angélica María Flores-Bustos, Ignacio Alejandro Molina-Villablanca, Joshua Emanuel Leyton-Vallejos

Abstract

Existing narrative extraction methods face a trade-off between coherence, interactivity, and multi-storyline support. Narrative Maps supports rich interaction and generates multiple storylines as a byproduct of its coverage constraints, though this comes at the cost of individual path coherence. Narrative Trails achieves high coherence through maximum capacity path optimization but provides no mechanism for user guidance or multiple perspectives. We introduce agenda-based narrative extraction, a method that bridges this gap by integrating large language models into the Narrative Trails pathfinding process to steer storyline construction toward user-specified perspectives. Our approach uses an LLM at each step to rank candidate documents based on their alignment with a given agenda while maintaining narrative coherence. Running the algorithm with different agendas yields different storylines through the same corpus. We evaluated our approach on a news article corpus using LLM judges with Claude Opus 4.5 and GPT 5.1, measuring both coherence and agenda alignment across 64 endpoint pairs and 6 agendas. LLM-driven steering achieves 9.9% higher alignment than keyword matching on semantic agendas (p=0.017), with 13.3% improvement on \textit{Regime Crackdown} specifically (p=0.037), while keyword matching remains competitive on agendas with literal keyword overlap. The coherence cost is minimal: LLM steering reduces coherence by only 2.2% compared to the agenda-agnostic baseline. Counter-agendas that contradict the source material score uniformly low (2.2-2.5) across all methods, confirming that steering cannot fabricate unsupported narratives.

Agenda-based Narrative Extraction: Steering Pathfinding Algorithms with Large Language Models

Abstract

Existing narrative extraction methods face a trade-off between coherence, interactivity, and multi-storyline support. Narrative Maps supports rich interaction and generates multiple storylines as a byproduct of its coverage constraints, though this comes at the cost of individual path coherence. Narrative Trails achieves high coherence through maximum capacity path optimization but provides no mechanism for user guidance or multiple perspectives. We introduce agenda-based narrative extraction, a method that bridges this gap by integrating large language models into the Narrative Trails pathfinding process to steer storyline construction toward user-specified perspectives. Our approach uses an LLM at each step to rank candidate documents based on their alignment with a given agenda while maintaining narrative coherence. Running the algorithm with different agendas yields different storylines through the same corpus. We evaluated our approach on a news article corpus using LLM judges with Claude Opus 4.5 and GPT 5.1, measuring both coherence and agenda alignment across 64 endpoint pairs and 6 agendas. LLM-driven steering achieves 9.9% higher alignment than keyword matching on semantic agendas (p=0.017), with 13.3% improvement on \textit{Regime Crackdown} specifically (p=0.037), while keyword matching remains competitive on agendas with literal keyword overlap. The coherence cost is minimal: LLM steering reduces coherence by only 2.2% compared to the agenda-agnostic baseline. Counter-agendas that contradict the source material score uniformly low (2.2-2.5) across all methods, confirming that steering cannot fabricate unsupported narratives.

Paper Structure

This paper contains 20 sections, 1 equation, 3 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Background: Coherence Graphs and Maximum Capacity Paths
  5. Agenda-Driven Pathfinding
  6. Baseline Methods
  7. Evaluation Framework
  8. Agenda Definitions
  9. Multi-Judge Evaluation
  10. Results
  11. Main Comparison: LLM-Driven vs. Baselines
  12. Visualization of Steering Effects
  13. Sensitivity Analysis
  14. Discussion
  15. Conclusion
  16. ...and 5 more sections

Figures (3)

  • Figure 1: Coherence vs. alignment percentage differences with respect to the maximum capacity baseline for the keyword-based and LLM-based methods.
  • Figure 2: Narrative trails visualization showing how different agendas steer paths through the 2D embedding space from a trial run of the algorithm. The neutral baseline (optimal bottleneck coherence) is compared against four agenda-driven paths. Endpoints and shared nodes are highlighted. Background contours show article density in the UMAP projection space, illustrating that different agendas steer paths through different topical regions of the corpus---for instance, the Regime Crackdown path traverses denser clusters of government-response articles, while Freedom Uprising follows a trajectory through protest-coverage clusters.
  • Figure 3: Equivalent narrative map showing the structure of extracted storylines from a trial run of the algorithm. All five paths (neutral baseline plus four agendas) flow from the source article at top to the target at bottom. Higher resolution version available here: https://figshare.com/s/1aff7f3727b7ad4fa9dc