Beyond Description: A Multimodal Agent Framework for Insightful Chart Summarization

• URL: http://arxiv.org/abs/2602.18731v1
• Date: Sat, 21 Feb 2026 06:17:37 GMT
• Title: Beyond Description: A Multimodal Agent Framework for Insightful Chart Summarization
• Authors: Yuhang Bai, Yujuan Ding, Shanru Lin, Wenqi Fan,
• Abstract summary: We propose a plan-and-execute multi-agent framework to uncover profound insights directly from chart images.<n>To overcome the lack of suitable benchmarks, we introduce ChartSummInsights, a new dataset featuring a diverse collection of real-world charts.
• Score: 18.33134893463544
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Summarize-Exemplify-Reflect: Data-driven Insight Distillation Empowers LLMs for Few-shot Tabular Classification [31.422359959517763]
  We introduce InsightTab, an insight distillation framework guided by principles of divide-and-conquer, easy-first, and reflective learning.<n>Our approach integrates rule summarization, strategic exemplification, and insight reflection through deep collaboration between LLMs and data modeling techniques.<n>The results demonstrate consistent improvement over state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-08-29T12:16:24Z)
• RADAR: A Reasoning-Guided Attribution Framework for Explainable Visual Data Analysis [37.42273691073023]
  Multimodal Large Language Models (MLLMs) offer promising capabilities for automated visual data analysis.<n>However, they provide no visibility into which parts of the visual data informed their conclusions.<n>We take the first major step towards evaluating and enhancing the capabilities of MLLMs to attribute their reasoning process.
  arXiv  Detail & Related papers  (2025-08-23T00:42:43Z)
• Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation [25.283739839182147]
  We show that training an MLLM with Chain-of-Thought (CoT) reasoning data can facilitate model adaptation in specialized vision tasks.<n>We propose Grounded Chain-of-Thought (GCoT), a simple bootstrapping-based approach that aims to inject grounding information into CoT data.<n>We evaluate our approach on five specialized vision tasks, which cover a variety of visual formats.
  arXiv  Detail & Related papers  (2025-07-03T17:59:29Z)
• IDA-Bench: Evaluating LLMs on Interactive Guided Data Analysis [60.32962597618861]
  IDA-Bench is a novel benchmark evaluating large language models in multi-round interactive scenarios.<n>Agent performance is judged by comparing its final numerical output to the human-derived baseline.<n>Even state-of-the-art coding agents (like Claude-3.7-thinking) succeed on 50% of the tasks, highlighting limitations not evident in single-turn tests.
  arXiv  Detail & Related papers  (2025-05-23T09:37:52Z)
• Chart-HQA: A Benchmark for Hypothetical Question Answering in Charts [62.45232157149698]
  We introduce a novel Chart Hypothetical Question Answering (HQA) task, which imposes assumptions on the same question to compel models to engage in counterfactual reasoning based on the chart content.<n> Furthermore, we introduce HAI, a human-AI interactive data synthesis approach that leverages the efficient text-editing capabilities of MLLMs alongside human expert knowledge to generate diverse and high-quality HQA data at a low cost.
  arXiv  Detail & Related papers  (2025-03-06T05:08:40Z)
• End-to-End Chart Summarization via Visual Chain-of-Thought in Vision-Language Models [0.0]
  This paper introduces End-to-End Visual Chain-of-Thought (V-CoT) for chart summarization.<n>Our method directly trains an LVLM to process chart images and generate textual summaries in an end-to-end fashion.<n>We incorporate a visual Chain-of-Thought mechanism through instruction fine-tuning, implicitly guiding the LVLM to perform visual reasoning steps.
  arXiv  Detail & Related papers  (2025-02-24T19:13:45Z)
• Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach [56.55633052479446]
  Web-scale visual entity recognition presents significant challenges due to the lack of clean, large-scale training data. We propose a novel methodology to curate such a dataset, leveraging a multimodal large language model (LLM) for label verification, metadata generation, and rationale explanation. Experiments demonstrate that models trained on this automatically curated data achieve state-of-the-art performance on web-scale visual entity recognition tasks.
  arXiv  Detail & Related papers  (2024-10-31T06:55:24Z)
• On Pre-training of Multimodal Language Models Customized for Chart Understanding [83.99377088129282]
  This paper explores the training processes necessary to improve MLLMs' comprehension of charts.<n>We introduce CHOPINLLM, an MLLM tailored for in-depth chart comprehension.
  arXiv  Detail & Related papers  (2024-07-19T17:58:36Z)
• Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning? An Extensive Investigation into the Capabilities and Limitations of LVLMs [11.19928977117624]
  Natural language is a powerful complementary modality of communication for data visualizations, such as bar and line charts. Various downstream tasks have been introduced recently such as chart question answering, chart summarization, and fact-checking with charts. These tasks pose a unique challenge, demanding both vision-language reasoning and a nuanced understanding of chart data tables, visual encodings, and natural language prompts. This paper presents the first comprehensive evaluation of the recently developed large vision language models (LVLMs) for chart understanding and reasoning tasks.
  arXiv  Detail & Related papers  (2024-06-01T01:43:30Z)
• Multi-modal Auto-regressive Modeling via Visual Words [96.25078866446053]
  We propose the concept of visual tokens, which maps the visual features to probability distributions over Large Multi-modal Models' vocabulary. We further explore the distribution of visual features in the semantic space within LMM and the possibility of using text embeddings to represent visual information.
  arXiv  Detail & Related papers  (2024-03-12T14:58:52Z)
• Summary-Oriented Vision Modeling for Multimodal Abstractive Summarization [63.320005222549646]
  Multimodal abstractive summarization (MAS) aims to produce a concise summary given the multimodal data (text and vision) We propose to improve the summary quality through summary-oriented visual features. Experiments on 44 languages, covering mid-high, low-, and zero-resource scenarios, verify the effectiveness and superiority of the proposed approach.
  arXiv  Detail & Related papers  (2022-12-15T09:05:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.