# DASH: A Bimodal Data Exploration Tool for Interactive Text and Visualizations

arXiv: https://arxiv.org/abs/2408.01011 · HTML: https://mlsanity.com/paper/2408.01011/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2024.

## Built on by (most cited first, 2 of 2)

- [DATAWEAVER: Authoring Data-Driven Narratives through the Integrated Composition of Visualization and Text](https://mlsanity.com/paper/2503.22946/) (2025)
- [WidgetVA: A Widget-Centric Framework and Benchmark for Agentic Visual Analytics](https://mlsanity.com/paper/2609.24094/) (2026)

## Abstract (arXiv)

Integrating textual content, such as titles, annotations, and captions, with visualizations facilitates comprehension and takeaways during data exploration. Yet current tools often lack mechanisms for integrating meaningful long-form prose with visual data. This paper introduces DASH, a bimodal data exploration tool that supports integrating semantic levels into the interactive process of visualization and text-based analysis. DASH operationalizes a modified version of Lundgard et al.'s semantic hierarchy model that categorizes data descriptions into four levels ranging from basic encodings to high-level insights. By leveraging this structured semantic level framework and a large language model's text generation capabilities, DASH enables the creation of data-driven narratives via drag-and-drop user interaction. Through a preliminary user evaluation, we discuss the utility of DASH's text and chart integration capabilities when participants perform data exploration with the tool.