We expect contributions from researchers on all aspects of narrative extraction, representation, analysis, and generation. This includes the extraction and formal representation of events, their temporal and causal relationships, and methods for temporal reasoning and ordering. Submissions focusing on narrative comprehension, such as the analysis of generated narratives, are also highly encouraged. Additionally, we welcome innovative approaches to presenting narrative information, including automatic timeline generation, multi-modal narrative summarization, and narrative visualization. Research addressing misinformation and the verification of extracted facts, evaluation methodologies, and the development of annotated datasets, annotation schemas, and evaluation metrics is particularly valued. Finally, we are especially interested in submissions that focus on low and medium-resource languages, as well as multilingual and cross-lingual narrative analysis.

Building on these themes, several pressing questions emerge within the field, offering valuable guidance for authors in shaping their submissions.How can we better integrate multimodal content - combining text, images, videos, and audio - into cohesive narratives? What strategies can reliably extract or generate accurate narratives from large, multi-genre, and multi-lingual datasets? How can systems dynamically adapt to real-time shifts in narratives as the volume of generated content grows? What methodologies can effectively annotate data and evaluate novel approaches, for complex tasks such as visualization but also for characterization of multi-lingual narratives? How can we guarantee the explainability, interpretability, and coherence of narratives across diverse domains and languages? To what extent can novel approaches be generalized to new tasks, genres, and languages with minimal effort? What ethical safeguards are essential to ensure that narrative extraction systems are not misused for propaganda or manipulation? How can challenges posed by ambiguous or contradictory information within narratives be addressed through innovative methods? What role do cultural and contextual nuances play in narrative extraction, and how can these be effectively incorporated into automated systems to ensure greater inclusivity? How can collaboration between human annotators and automated systems be optimized to achieve more accurate, nuanced narrative understanding? How can systems generate concise, evidence-backed explanations to justify the dominant narrative while remaining grounded in the source text?

Full Papers

up to 8 pages + references

Original and high-quality unpublished contributions to the theory and practical aspects of the narrative extraction task. Full papers should introduce existing approaches, describe the methodology and the experiments conducted in detail. Negative result papers to highlight tested hypotheses that did not get the expected outcome are also welcomed.

Short Papers

up to 5 pages + references

Unpublished short papers describing work in progress; position papers introducing a new point of view, a research vision or a reasoned opinion on the workshop topics; and dissemination papers describing project ideas, ongoing research lines, case studies or summarized versions of previously published papers in high-quality conferences/journals that is worthwhile sharing with the Text2Story community, but where novelty is not a fundamental issue.

Demos | Resource Papers

up to 5 pages + references

Unpublished papers presenting research/industrial demos; papers describing important resources (datasets or software packages) to the Text2Story community;

Papers must be submitted electronically in PDF format through Easy Chair . All submissions must be in English and formatted according to the one-column CEUR-ART style with no page numbers. Templates, either in Word or LaTeX, can be found in the following zip folder . There is also an Overleaf page for LaTeX users.

IMPORTANT: Please include between brackets the type of submission (full; negative results; work in progress; demo and resource; position; dissemination) in the paper title.

Papers submitted to Text2Story 2025 should be original work and different from papers that have been previously published, accepted for publication, or that are under review at other venues. Exceptions to this rule are "dissemination papers". Pre-prints submitted to ArXiv are eligible.

All papers will be refereed through a double-blind peer-review process by at least two members of the programme committee. The accepted papers will appear in the proceedings published at CEUR workshop proceedings (indexed in Scopus and DBLP) as long as they don't conflict with previous publication rights.