  Representing structured text from complex documents typically calls for
different machine learning techniques, such as language models for paragraphs
and convolutional neural networks (CNNs) for table extraction, which prohibits
drawing links between text spans from different content types. In this article
we propose a model that approximates the human reading pattern of a document
and outputs a unique semantic representation for every text span irrespective
of the content type they are found in. We base our architecture on a graph
representation of the structured text, and we demonstrate that not only can we
retrieve semantically similar information across documents but also that the
embedding space we generate captures useful semantic information, similar to
language models that work only on text sequences.
