In this paper, we present a novel neural graph matching approach applied to
document comparison. Document comparison is a common task in the legal and
financial industries. In some cases, the most important differences may be the
addition or omission of words, sentences, clauses, or paragraphs. However, it
is a challenging task without recording or tracing whole edited process. Under
many temporal uncertainties, we explore the potentiality of our approach to
proximate the accurate comparison to make sure which element blocks have a
relation of edition with others. In beginning, we apply a document layout
analysis that combining traditional and modern technics to segment layout in
blocks of various types appropriately. Then we transform this issue to a
problem of layout graph matching with textual awareness. About graph matching,
it is a long-studied problem with a broad range of applications. However,
different from previous works focusing on visual images or structural layout,
we also bring textual features into our model for adapting this domain.
Specifically, based on the electronic document, we introduce an encoder to deal
with the visual presentation decoding from PDF. Additionally, because the
modifications can cause the inconsistency of document layout analysis between
modified documents and the blocks can be merged and split, Sinkhorn divergence
is adopted in our graph neural approach, which tries to overcome both these
issues with many-to-many block matching. We demonstrate this on two categories
of layouts, as follows., legal agreement and scientific articles, collected
from our real-case datasets