Data augmentation on graphs for table type classification
This work addresses the problem of interpreting and categorizing tables in scientific papers for scholars, but it is incremental as it builds on existing graph-based methods with data augmentation.
The paper tackles table type classification by using a Graph Neural Network to exploit table structure and proposes data augmentation techniques on table graphs to address limited annotated data, achieving promising preliminary results on the Tab2Know dataset.
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.