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.

本文提出了一种基于语义解析的跨页关系提取方法，并提出了一个端到端的跨页依赖性抽取方法。实验结果表明，我们的方法大大提高了语义解析的准确性，并且对多任务处理中的页面嵌入也有帮助。