Compendia: Automated Visual Storytelling Generation from Online Article Collection.

2026-02-07

IEEE Transactions on Visualization and Computer Graphics

AI Summary

This work proposes an end-to-end approach that automatically transforms fragmented, unstructured online articles into user-centered, interactive visual data narratives. To address the challenge of information fragmentation across multiple textual sources, the system integrates information retrieval, quantitative fact extraction and verification, topic clustering with fusion, and scrollytelling generation—enabling, for the first time, the fully automated construction of coherent data stories from raw articles. Experimental results demonstrate high accuracy in fact extraction and organization. Furthermore, a user study involving 16 participants confirms that the generated visual narratives excel in usability, informational effectiveness, and narrative engagement.

Technology Category

• Natural Language Processing: Information Extraction
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Web Mining and Content Analysis: Web data visualization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

In the digital age, readers value quantitative journalism that is clear, concise, analytical, and human-centred. To understand complex topics, they often piece together scattered facts from multiple articles. Visual storytelling can transform fragmented information into clear, engaging narratives, yet its use with unstructured online articles remains largely unexplored. To fill this gap, we present Compendia, an automated system that analyzes online articles in response to a user's query and generates a coherent data story tailored to the user's informational needs. through two modules covering addresses key challenges of storytelling from unstructured text through two modules covering: Online Article Retrieval, which gathers relevant articles; Data Fact Extraction, which identifies, validates, and refines quantitative facts; Fact Organization, which clusters and merges related facts into coherent thematic groups; and Visual Storytelling, which transforms the organized facts into narratives with visualizations in an interactive scrollytelling interface. We evaluated Compendia through a quantitative analysis, confirming the accuracy in fact extraction and organization, and through two user studies with 16 participants, demonstrating its usability, effectiveness, and ability to produce engaging visual stories for open-ended queries.

Problem

• visual storytelling
• unstructured text
• online articles
• data narrative
• information integration

Innovation

• automated visual storytelling
• data fact extraction
• unstructured text analysis
• interactive scrollytelling
• quantitative journalism