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Leveraging Foundation Models for Crafting Narrative Visualization: A Survey

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arxiv 2401.14010 v4 pith:ZTVLXNRO submitted 2024-01-25 cs.HC

classification cs.HC
keywords foundationmodelsnarrativevisualizationcraftingliteraturesurveytask
verification ladder T0 review T1 audit T2 compute T3 formal

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Narrative visualization transforms data into engaging stories, making complex information accessible to a broad audience. Foundation models, with their advanced capabilities such as natural language processing, content generation, and multimodal integration, hold substantial potential for enriching narrative visualization. Recently, a collection of techniques have been introduced for crafting narrative visualizations based on foundation models from different aspects. We build our survey upon 66 papers to study how foundation models can progressively engage in this process and then propose a reference model categorizing the reviewed literature into four essential phases: Analysis, Narration, Visualization, and Interaction. Furthermore, we identify eight specific tasks (e.g. Insight Extraction and Authoring) where foundation models are applied across these stages to facilitate the creation of visual narratives. Detailed descriptions, related literature, and reflections are presented for each task. To make it a more impactful and informative experience for diverse readers, we discuss key research problems and provide the strengths and weaknesses in each task to guide people in identifying and seizing opportunities while navigating challenges in this field.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Pluto: Authoring Semantically Aligned Text and Charts for Data-Driven Communication

    cs.HC 2025-02 conditional novelty 6.0 of 10

    Pluto is a mixed-initiative system that uses chart features and user text to generate, complete, and align descriptions and chart design for data-driven communication.

  2. ChartInsighter: An Approach for Mitigating Hallucination in Time-series Chart Summary Generation with A Benchmark Dataset

    cs.CL 2025-01 conditional novelty 6.0 of 10

    A multi-agent LLM pipeline with external computation and self-consistency checking produces time-series chart summaries with fewer annotated hallucinations than GPT-4 or VL2NL on the authors' new benchmark.

  3. Jupybara: Operationalizing a Design Space for Actionable Data Analysis and Storytelling with LLMs

    cs.HC 2025-01 conditional novelty 5.0 of 10

    Jupybara is an LLM-powered Jupyter extension that operationalizes a semantic, rhetorical, and pragmatic design space for actionable data analysis and storytelling.

  4. MDSF: Context-Aware Multi-Dimensional Data Storytelling Framework based on Large language Model

    cs.CL 2025-01 reject novelty 4.0 of 10

    MDSF is an LLM-based framework for automated data insight ranking and storytelling that, by its own reported results, does not outperform GPT-4 on ranking and most narrative metrics.

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