NarratorVis：透過規則式方法與大型語言模型實現自動化、聽眾適應與情境感知之視覺資料敘事系統

Abstract

視覺化資料敘事（Visual Data Storytelling）結合資料、敘事與視覺化元素，以有效的傳達資訊。然而，當我們進行簡報或是資訊傳遞的時候，如何判斷資料中應強調的面向是一項挑戰，因為不同的受眾可能所需要關注的重點不盡相同，而缺乏敘事經驗的使用者，往往難以辨識對於各類受眾而言最具意義的內容。此外，不同的溝通目的，例如說服、知識傳遞或情感共鳴，皆需採用不同的敘事策略。現有工具多聚焦在資料視覺化以及視覺化的故事敘事，但是在支援使用者依據溝通意圖選擇適當敘事模式或產生具受眾適應性與語境感知能力的故事方面，仍有明顯不足。為解決上述問題，本研究提出 NarratorVis 一套能自動化生成具受眾意識的視覺化資料敘事系統。使用者可指定目標受眾、敘事目的、知識深度與預期簡報長度等關鍵參數，系統將其轉化為敘事建構的語境指引。NarratorVis 採用規則式方法自表格資料中擷取相關事實，並結合大型語言模型（LLMs），生成具一致性之敘事文本與視覺化圖表，進而產出具受眾適應性的敘事。此外，系統亦提供評分機制與編輯介面，以支援使用者後續修改與調整。本研究亦進行一項使用者研究，邀請五位參與者使用系統進行資料敘事任務，並透過半結構式訪談收集其使用經驗、滿意度及對系統效益的看法。研究結果顯示，NarratorVis 有助於使用者有效依據不同受眾需求進行資料故事設計，並提升其在簡報準備上的信心。

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 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 involving 5 participants, who used the system to complete storytelling tasks, followed by semi-structured interviews to collect feedback on their experiences, satisfaction, and perceived usefulness. The results suggest that NarratorVis helps users effectively tailor data stories to diverse audiences and enhances their confidence in presentation preparation.