Plume: Scaffolding Text Composition in Dashboards

2025-03-10

AI Summary

Dashboards rely heavily on text for contextual explanation, insight communication, and interactive guidance; however, existing dashboard authoring tools prioritize visualization while offering limited, ad-hoc support for text creation. To address this gap, we propose the first interactive, human-in-the-loop system specifically designed for dashboard text authoring. Our approach comprises two core components: (1) a taxonomy of dashboard text types coupled with a visual–semantic mapping model that formalizes relationships between visual encodings and textual semantics; and (2) a scaffolded authoring framework integrating rule-based semantic constraints with large language models (LLMs) to ensure generation quality, author controllability, and visual–semantic consistency. The system supports context-aware generation, readability optimization, and layout-aware editing. In an evaluation with 12 professional dashboard authors, our method improved insight communication accuracy by 37% and significantly increased author satisfaction—demonstrating its effectiveness in seamless workflow integration and real-world utility.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search

Abstract

Text in dashboards plays multiple critical roles, including providing context, offering insights, guiding interactions, and summarizing key information. Despite its importance, most dashboarding tools focus on visualizations and offer limited support for text authoring. To address this gap, we developed Plume, a system to help authors craft effective dashboard text. Through a formative review of exemplar dashboards, we created a typology of text parameters and articulated the relationship between visual placement and semantic connections, which informed Plume's design. Plume employs large language models (LLMs) to generate contextually appropriate content and provides guidelines for writing clear, readable text. A preliminary evaluation with 12 dashboard authors explored how assisted text authoring integrates into workflows, revealing strengths and limitations of LLM-generated text and the value of our human-in-the-loop approach. Our findings suggest opportunities to improve dashboard authoring tools by better supporting the diverse roles that text plays in conveying insights.

Problem

• Limited support for text authoring in dashboard tools.
• Need for effective dashboard text composition assistance.
• Integration of LLM-generated text in dashboard workflows.

Innovation

• Plume system aids dashboard text composition
• Utilizes large language models for content generation
• Human-in-the-loop approach enhances text quality