Neural Formatting for Spreadsheet Tables - Dong, Haoyu; Wang, Jinyu; Fu, Zhouyu; Han, Shi; Zhang, Dongmei (CIKM'20 (among five best paper nominees in full research track), 2020)
Spreadsheets are popular and widely used for data presentation and management, where users create tables in various structures to organize and present data. Table formatting is an important yet tedious task for better exhibiting table structures and data relationships. However, without the aid of intelligent tools, manual formatting remains a tedious and time-consuming task. In this paper, we propose CellGAN, a neural formatting model for learning and recommending formats of spreadsheet tables. Based on a novel conditional generative adversarial network (cGAN) architecture, CellGAN learns table formatting from real-world spreadsheet tables in a self-supervised fashion without requiring human labeling. In CellGAN we devise two mechanisms, row/column-wise pooling and local refinement network, to address challenges from the spreadsheet domain. We evaluate the effectiveness of CellGAN against real-world datasets using both quantitative metrics and human perception studies. The results indicate remarkable performance gains over rule-based methods, graphical models or direct application of the state-of-the-art cGANs used in visual synthesis tasks. Neural Formatting is the first step towards auto-formatting for spreadsheet tables with promising results.