Beyond Description: A Multimodal Agent Framework for Insightful Chart Summarization
This work addresses the need for deeper insights in chart summarization to enhance data accessibility, though it appears incremental by building on existing MLLM methods.
The paper tackles the problem of chart summarization by proposing a multi-agent framework that leverages Multimodal Large Language Models to generate insightful summaries from chart images, and introduces a new dataset for evaluation, showing significant performance improvements.
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.