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All Journal Buletin Ilmiah Sarjana Teknik Elektro International Journal of Engineering, Science and Information Technology
Erna Daniati
Universitas Negeri Malang
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Survey and Challenges: Event Extraction of Story Narrative in NLP Approach Erna Daniati; Aji Prasetya Wibawa; Wahyu Sakti Gunawan Irianto; Andrew Nafalski
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.15534

Abstract

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.
Building a Narrative Event Dataset from Andersen’s Fairy Tales for Literary and Computational Analysis Erna Daniati; Aji Prasetya Wibawa; Wahyu Sakti Gunawan Irianto
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.910

Abstract

This paper describes building a narrative event dataset for the entire set of 153 fairy tales written by Hans Christian Andersen as?a resource for literary analysis and computational research. The corpus is?built up through semi-automatic annotation for important narrative events: character actions, period transitions, causal communications, and story themes. Each event is augmented with? metadata such as event type, event participants, event temporality (order) and event thematic relevance. This computer-readable structured data is helpful for NLP applications like event detection and temporal reasoning. Still, it supports in-depth literary?studies of plot structures, moral themes and character archetypes in Andersen's stories. Linking the digital humanities with the domain of computational linguistics, the dataset can be jointly used in inter-disciplinary research, and has the potential to reveal new aspects of classical narrative forms and how these findings?and developments can be usefully integrated in AI-supported storytelling systems.
Co-Authors Aji Prasetya Wibawa Andrew Nafalski Wahyu Sakti Gunawan Irianto