  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 text-to-table as a sequence-to-sequence (seq2seq) problem.
We first employ a seq2seq model fine-tuned 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. We consider text-to-table as an inverse problem of
the well-studied table-to-text, and make use of four existing table-to-text
datasets in our experiments on text-to-table. Experimental results show that
the vanilla seq2seq model can outperform the baseline methods of using relation
extraction and named entity extraction. The results also show that our method
can further boost the performances of the vanilla seq2seq model. We further
discuss the main challenges of the proposed task. The code and data are
available at https://github.com/shirley-wu/text_to_table.
