[2603.29661] Agenda-based Narrative Extraction: Steering Pathfinding Algorithms with Large Language Models

Computer Science > Computation and Language arXiv:2603.29661 (cs) [Submitted on 31 Mar 2026] Title:Agenda-based Narrative Extraction: Steering Pathfinding Algorithms with Large Language Models Authors: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 View a PDF of the paper titled Agenda-based Narrative Extraction: Steering Pathfinding Algorithms with Large Language Models, by Brian Felipe Keith-Norambuena and 6 other authors View PDF HTML (experimental) 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 ...