In the contemporary era of widespread online recruitment, resume
understanding has been widely acknowledged as a fundamental and crucial task,
which aims to extract structured information from resume documents
automatically. Compared to the traditional rule-based approaches, the
utilization of recently proposed pre-trained document understanding models can
greatly enhance the effectiveness of resume understanding. The present
approaches have, however, disregarded the hierarchical relations within the
structured information presented in resumes, and have difficulty parsing
resumes in an efficient manner. To this end, in this paper, we propose a novel
model, namely ERU, to achieve efficient resume understanding. Specifically, we
first introduce a layout-aware multi-modal fusion transformer for encoding the
segments in the resume with integrated textual, visual, and layout information.
Then, we design three self-supervised tasks to pre-train this module via a
large number of unlabeled resumes. Next, we fine-tune the model with a
multi-granularity sequence labeling task to extract structured information from
resumes. Finally, extensive experiments on a real-world dataset clearly
demonstrate the effectiveness of ERU.

本文提出了一种名为 ERU 的新型模型，通过引入多模态融合变压器编码简历段落的布局感知信息，设计三个自监督任务用于无标签简历的预训练，并通过多粒度序列标记任务对模型进行微调，从简历中提取结构化信息，实验证明 ERU 的有效性。