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[2602.18731] Beyond Description: A Multimodal Agent Framework for Insightful Chart Summarization
Summary
This article presents a novel framework for chart summarization using a multimodal agent approach, enhancing data accessibility and insight extraction from visual data.
Why It Matters
The ability to summarize charts effectively is critical for data analysis and interpretation. This framework leverages advanced AI techniques to provide deeper insights, addressing limitations of existing methods and contributing to the field of data visualization and machine learning.
Key Takeaways
- Introduces a new framework for insightful chart summarization.
- Utilizes Multimodal Large Language Models (MLLMs) for deeper data insights.
- Presents a new dataset, ChartSummInsights, for benchmarking summarization tasks.
- Demonstrates significant performance improvements in summarization tasks.
- Addresses the gap in existing methods that focus on low-level data descriptions.
Computer Science > Artificial Intelligence arXiv:2602.18731 (cs) [Submitted on 21 Feb 2026] Title:Beyond Description: A Multimodal Agent Framework for Insightful Chart Summarization Authors:Yuhang Bai, Yujuan Ding, Shanru Lin, Wenqi Fan View a PDF of the paper titled Beyond Description: A Multimodal Agent Framework for Insightful Chart Summarization, by Yuhang Bai and 2 other authors View PDF HTML (experimental) Abstract:Chart summarization is crucial for enhancing data accessibility and the efficient consumption of information. However, existing methods, including those with Multimodal Large Language Models (MLLMs), primarily focus on low-level data descriptions and often fail to capture the deeper insights which are the fundamental purpose of data visualization. To address this challenge, we propose Chart Insight Agent Flow, a plan-and-execute multi-agent framework effectively leveraging the perceptual and reasoning capabilities of MLLMs to uncover profound insights directly from chart images. Furthermore, to overcome the lack of suitable benchmarks, we introduce ChartSummInsights, a new dataset featuring a diverse collection of real-world charts paired with high-quality, insightful summaries authored by human data analysis experts. Experimental results demonstrate that our method significantly improves the performance of MLLMs on the chart summarization task, producing summaries with deep and diverse insights. Comments: Subjects: Artificial Intelligence (cs.AI) Cite as:...