Survey and Challenges: Event Extraction of Story Narrative in NLP Approach

Event extraction from story narratives remains a challenging yet underexplored area in natural language processing due to narrative complexity including implicit causality long-range dependencies and temporal ambiguity. This study addresses the research question: How have NLP and deep learning approaches been applied to extract events from story narratives and what gaps persist. Following the PRISMA 2020 guidelines we systematically reviewed 12 peer-reviewed studies published between 2017 and 2024. Our analysis reveals growing adoption of transformer-based models such as BERT alongside emerging architectures like DEEIA and PAIE which leverage prompt-based learning and event-specific contextual aggregation. Commonly used datasets include ROCStories and custom narrative corpora though few are standardized. Key challenges involve handling implicit events limited annotated data cross-domain generalization and integration of commonsense reasoning. The main contribution of this review is the first structured synthesis of event extraction techniques specifically for story narratives using a rigorous systematic methodology. We highlight the need for document-level modeling narrative-aware evaluation metrics and low-resource adaptation strategies. This work provides a foundation for future research aiming to bridge narrative understanding with robust event-centric NLP systems.