Beyond Description: A Multimodal Agent Framework for Insightful Chart Summarization

2026-02-21

AI Summary

This work addresses the limitation of existing chart summarization methods, which often produce shallow, data-centric descriptions lacking deeper analytical insights. To overcome this, we propose Chart Insight Agent Flow—a novel planning-and-execution-based multi-agent framework that introduces, for the first time, a collaborative multi-agent mechanism specifically designed for generating deep, insightful chart summaries. By integrating multimodal large language models (MLLMs), our approach synergistically combines visual perception with logical reasoning to enable comprehensive analysis and high-level summarization of chart images. We also present ChartSummInsights, the first human-expert-annotated dataset of high-quality chart insight summaries. Experimental results demonstrate that our method significantly outperforms current state-of-the-art techniques on this benchmark, producing summaries that are more insightful, diverse, and analytically valuable, thereby substantially enhancing the accessibility and utility of chart-based information.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Natural Language Processing: Summarization
• Multiagent Systems: Multiagent Planning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• chart summarization
• insight generation
• multimodal large language models
• data visualization

Innovation

• multimodal agent framework
• chart summarization
• insight generation
• MLLMs
• ChartSummInsights