Plume: Scaffolding Text Composition in Dashboards

要旨

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.

著者
Maxim Lisnic
University of Utah, Salt Lake City, Utah, United States
Vidya Setlur
Tableau Research, Palo Alto, California, United States
Nicole Sultanum
Tableau Research, Seattle, Washington, United States
DOI

10.1145/3706598.3713580

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713580

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Text Entry

Annex Hall F205
7 件の発表
2025-04-29 23:10:00
2025-04-30 00:40:00
日本語まとめ
読み込み中…