发布首个支持跨页表格提取的大规模数据集，推动文档理解发展
PubTables-v2: A new large-scale dataset for full-page and multi-page table extraction
- 构建跨页与单页统一的表格提取数据集
- 全文档多页提取任务上大模型领先0.354分，小模型在特定任务可反超
- 适合做文档理解、表格识别的研究者和开发者
表格提取（TE）是文档理解的关键挑战。传统方法先检测表格，再识别其结构。近年来，视觉语言模型（VLMs）等方法兴起，可直接在整页或文档上下文中提取表格。然而，标注数据缺乏限制了进展。为此，我们构建了新的大规模数据集PubTables-v2，统一了不同上下文层级的表格提取任务，并首次建立多页表格提取基准。评估显示，当前前沿模型在最复杂任务（全文档多页提取）上比小模型高出0.354分（GriTS_Con），但在窄任务（裁剪表格提取）中，通过针对性训练，小模型可缩小甚至反超差距（-0.056分）。数据已开放于https://huggingface.co/datasets/kensho/PubTables-v2，代码与模型将陆续发布。
原文摘要 · Abstract (English)
Table extraction (TE) is a key challenge in document understanding. Traditional approaches detect tables first, then recognize their structure. Recently, interest has surged in developing methods, such as vision-language models (VLMs), to extract tables directly in their full page or document context. However, a lack of annotated data has made progress difficult to demonstrate. To address this, we create a new large-scale dataset, PubTables-v2. PubTables-v2 unifies TE across various levels of surrounding context and, notably, is the first benchmark for multi-page TE. Our evaluations reveal that while current frontier models strongly outperform ($+0.354\ \textrm{GriTS}_\textrm{Con}$) small models on the most complex task (full-document multi-page TE), this gap can be closed or even reversed ($-0.056\ \textrm{GriTS}_\textrm{Con}$) on narrower tasks (cropped table extraction) with targeted training. Data is available at https://huggingface.co/datasets/kensho/PubTables-v2. Code and models will be released.
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