Text-to-Table: A New Way of Information Extraction

Abstract

We study a new problem setting of information extraction (IE), referred to as text-to-table . In text-to-table, given a text, one creates a table or several tables expressing the main content of the text, while the model is learned from text-table pair data. The problem setting differs from those of the existing methods for IE. First, the extraction can be carried out from long texts to large tables with complex structures. Second, the extraction is entirely data-driven, and there is no need to explicitly define the schemas. As far as we know, there has been no previous work that studies the problem. In this work, we formalize textto-table as a sequence-to-sequence (seq2seq) problem. We first employ a seq2seq model finetuned from a pre-trained language model to perform the task. We also develop a new method within the seq2seq approach, exploiting two additional techniques in table generation: table constraint and table relation embeddings.