  Page-level analysis of documents has been a topic of interest in digitization
efforts, and multimodal approaches have been applied to both classification and
page stream segmentation. In this work, we focus on capturing finer semantic
relations between pages of a multi-page document. To this end, we formalize the
task as semantic parsing of interpage relations and we propose an end-to-end
approach for interpage dependency extraction, inspired by the dependency
parsing literature. We further design a multi-task training approach to jointly
optimize for page embeddings to be used in segmentation, classification, and
parsing of the page dependencies using textual and visual features extracted
from the pages. Moreover, we also combine the features from two modalities to
obtain multimodal page embeddings. To the best of our knowledge, this is the
first study to extract rich semantic interpage relations from multi-page
documents. Our experimental results show that the proposed method increased LAS
by 41 percentage points for semantic parsing, increased accuracy by 33
percentage points for page stream segmentation, and 45 percentage points for
page classification over a naive baseline.
