  Distilling large, unstructured text into a structured, condensed form such as
tables is an open research problem. One of the primary challenges in
automatically generating tables is ensuring their syntactic validity. Prior
approaches address this challenge by including additional parameters in the
Transformer's attention mechanism to attend to specific rows and column
headers. In contrast to this single-stage method, this paper presents a
two-stage approach called Generative Tables (gTBLS). The first stage infers
table structure (row and column headers) from the text. The second stage
formulates questions using these headers and fine-tunes a causal language model
to answer them. Furthermore, the gTBLS approach is amenable to the utilization
of pre-trained Large Language Models in a zero-shot configuration, presenting a
solution for table generation in situations where fine-tuning is not feasible.
gTBLS improves prior approaches by up to 10% in BERTScore on the table
construction task and up to 20% on the table content generation task of the
E2E, WikiTableText, WikiBio, and RotoWire datasets.
