用双分支框架提升图表数据纠错与摘要准确性
ChartLens: A Dual-Branch Framework for Chart Data Correction and Factual Summary Refinement
- 双分支设计：一个专攻数据校正，一个专注摘要优化
- 测试集上综合得分69.10，排名第一，显著提升事实准确性
- 适合需要高精度图表理解的科研与工业场景
本文提出针对DataMFM挑战赛第二赛道（图表理解）的冠军解决方案。该赛道要求模型从图表图像中恢复结构化数据并生成忠实的自然语言摘要。为应对数据提取与事实叙述的双重需求，我们提出ChartLens——一种双分支框架，包含结构感知的CSV验证与校正模块（SAVC）和文本保留引导的摘要优化模块（TRSR）。SAVC通过验证与修正提升结构化数据可靠性，TRSR则通过保留图表中的关键文字与数值证据来增强摘要生成。结合模型适配、基于校正的生成及OCR辅助的证据定位，ChartLens在结构化数据恢复与摘要事实性上均取得提升。最终系统在测试集上获得69.10的综合得分，位列第一，验证了其在精准图表理解上的有效性。代码将开源至https://github.com/iLearn-Lab/CVPRW26-ChartLens。
原文摘要 · Abstract (English)
In this report, we present our champion solution for the DataMFM Challenge Track 2: Chart Understanding. This track requires models to recover structured chart data and generate faithful natural-language summaries from chart images. To address the complementary requirements of accurate data extraction and factual narration, we propose ChartLens, a dual-branch framework for chart data correction and summary refinement. ChartLens consists of two key modules: Structure-Aware CSV Verification and Correction (SAVC) and Text-Retention-Guided Summary Refinement (TRSR). SAVC improves the reliability of structured data extraction through verification and correction, while TRSR enhances summary generation by preserving critical textual and numerical evidence from charts. By combining model adaptation, correction-based generation, and OCR-assisted evidence grounding, ChartLens improves both structured data recovery and summary factuality. On the test set, our final system achieves an overall score of 69.10 and ranks first in Track 2, demonstrating its effectiveness for accurate chart understanding. Our code will be released at: https://github.com/iLearn-Lab/CVPRW26-ChartLens.
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