NarratorVis: Automated audience-adaptive context-aware visual data storytelling via rule-based approach and large language model

Abstract

Visual data storytelling combines data, narrative, and visualization to convey insights effectively. However, determining which aspects of a dataset to emphasize can be challenging, as different audiences may require different focal points and individuals without storytelling expertise often struggle to identify what is most relevant for each group. Moreover, different communication goals, such as persuasion, knowledge transfer, or emotional engagement, require distinct storytelling strategies. Yet, existing tools rarely support users in selecting narrative patterns that align with their intent or in generating audience-specific, context-aware stories. To address this gap, we introduce NarratorVis, a system that automates audience-aware visual data storytelling. NarratorVis allows users to specify key storytelling parameters such as target audience, purpose, knowledge depth, and desired duration. These parameters are transformed into contextual guidance for story construction. The system extracts relevant facts from tabular data using rule-based logic and generates coherent narratives with visualizations, assisted by Large Language Models (LLMs) to produce fluent and audience-adaptive text. A scoring system and editing interface support further refinement. We conducted a user study in which participants used the system to complete storytelling tasks, followed by semi-structured interviews to gather feedback on their experiences, satisfaction, and perceived usefulness. The findings indicate that NarratorVis supports users in tailoring data stories to diverse audiences and increases their confidence in presentation preparation.