The attention to table understanding using recent natural language models has been growing. However, most related works tend to focus on learning the structure of the table directly. Just as humans improve their understanding of sentences by comparing them, they can also enhance their understanding by comparing tables. With this idea, in this paper, we introduce ACCIO, tAble understanding enhanCed via Contrastive learnIng with aggregatiOns, a novel approach to enhancing table understanding by contrasting original tables with their pivot summaries through contrastive learning. ACCIO trains an encoder to bring these table pairs closer together. Through validation via column type annotation, ACCIO achieves competitive performance with a macro F1 score of 91.1 compared to state-of-the-art methods. This work represents the first attempt to utilize pairs of tables for table embedding, promising significant advancements in table comprehension. Our code is available at https://github.com/whnhch/ACCIO/.

翻译：近年来，利用自然语言模型进行表格理解的研究日益受到关注。然而，大多数相关工作往往直接聚焦于学习表格的结构。正如人类通过比较句子来提升理解能力一样，通过比较表格也能增强对表格的理解。基于这一思路，本文提出ACCIO（通过聚合对比学习增强表格理解），这是一种通过对比学习将原始表格与其枢轴摘要进行对比以增强表格理解的新方法。ACCIO训练一个编码器，使这些表格对在表示空间中更加接近。通过列类型标注任务验证，ACCIO取得了具有竞争力的性能，其宏F1分数达到91.1，与现有最先进方法相当。这项工作是首次尝试利用表格对进行表格嵌入，有望在表格理解领域取得显著进展。我们的代码发布于https://github.com/whnhch/ACCIO/。