Table extraction from PDF and image documents is a ubiquitous task in the real-world. Perfect extraction quality is difficult to achieve with one single out-of-box model due to (1) the wide variety of table styles, (2) the lack of training data representing this variety and (3) the inherent ambiguity and subjectivity of table definitions between end-users. Meanwhile, building customized models from scratch can be difficult due to the expensive nature of annotating table data. We attempt to solve these challenges with TableLab by providing a system where users and models seamlessly work together to quickly customize high-quality extraction models with a few labelled examples for the user's document collection, which contains pages with tables. Given an input document collection, TableLab first detects tables with similar structures (templates) by clustering embeddings from the extraction model. Document collections often contain tables created with a limited set of templates or similar structures. It then selects a few representative table examples already extracted with a pre-trained base deep learning model. Via an easy-to-use user interface, users provide feedback to these selections without necessarily having to identify every single error. TableLab then applies such feedback to finetune the pre-trained model and returns the results of the finetuned model back to the user. The user can choose to repeat this process iteratively until obtaining a customized model with satisfactory performance.
SciDaSynth: Interactive Structured Data Extraction from Scientific Literature with Large Language Model
Bosch Research North America（博世北美研究院）；Weill Cornell Medicine（威尔康奈尔医学院）；Cornell University（康奈尔大学）；Hong Kong University of Science and Technology（香港科技大学）
Collection Space Navigator: An Interactive Visualization Interface for Multidimensional Datasets
Tallinn University（塔林大学）；School of Digital Technologies, Tallinn University（塔林大学数字技术学院）；Baltic Film, Media and Arts School, Tallinn University（塔林大学波罗的海电影、媒体与艺术学院）；School of Humanities, Tallinn University（塔林大学人文学院）
针对多维数据相关视觉数字工件集合探索分析的挑战，本文提出开源的Collection Space Navigator（CSN）交互式可视化工具，结合二维投影与多维过滤器，支持缩放、投影转换等操作，助力用户探索、整理集合并反馈见解。
