Data augmentation on graphs for table type classification

TL;DR

This paper introduces a graph neural network approach for classifying table types in scientific documents, utilizing data augmentation techniques to improve performance on limited annotated datasets.

Contribution

It presents a novel graph-based method for table classification and proposes data augmentation techniques tailored for graph-structured table data.

Findings

Promising preliminary results with the proposed method.

Effective data augmentation improves classification performance.

Utilizes table structure for message passing in GNNs.

Abstract

Tables are widely used in documents because of their compact and structured representation of information. In particular, in scientific papers, tables can sum up novel discoveries and summarize experimental results, making the research comparable and easily understandable by scholars. Since the layout of tables is highly variable, it would be useful to interpret their content and classify them into categories. This could be helpful to directly extract information from scientific papers, for instance comparing performance of some models given their paper result tables. In this work, we address the classification of tables using a Graph Neural Network, exploiting the table structure for the message passing algorithm in use. We evaluate our model on a subset of the Tab2Know dataset. Since it contains few examples manually annotated, we propose data augmentation techniques directly on the table graph structures. We achieve promising preliminary results, proposing a data augmentation method suitable for graph-based table representation.