Scientific literature contain important information related to cutting-edge
innovations in diverse domains. Advances in natural language processing have
been driving the fast development in automated information extraction from
scientific literature. However, scientific literature is often available in
unstructured PDF format. While PDF is great for preserving basic visual
elements, such as characters, lines, shapes, etc., on a canvas for presentation
to humans, automatic processing of the PDF format by machines presents many
challenges. With over 2.5 trillion PDF documents in existence, these issues are
prevalent in many other important application domains as well.
Our ICDAR 2021 Scientific Literature Parsing Competition (ICDAR2021-SLP) aims
to drive the advances specifically in document understanding. ICDAR2021-SLP
leverages the PubLayNet and PubTabNet datasets, which provide hundreds of
thousands of training and evaluation examples. In Task A, Document Layout
Recognition, submissions with the highest performance combine object detection
and specialised solutions for the different categories. In Task B, Table
Recognition, top submissions rely on methods to identify table components and
post-processing methods to generate the table structure and content. Results
from both tasks show an impressive performance and opens the possibility for
high performance practical applications.

本文介绍了科技文献对自然语言处理的挑战和 ICDAR 2021 科技文献解析竞赛的使用，并展示了该竞赛在文档布局识别和表格识别方面的高性能成果。

ICDAR 2021 科学文献解析竞赛

ICDAR 2021 Competition on Scientific Literature Parsing

State-of-the-art solutions for Natural Language Processing (NLP) are able to
capture a broad range of contexts, like the sentence-level context or
document-level context for short documents. But these solutions are still
struggling when it comes to longer, real-world documents with the information
encoded in the spatial structure of the document, such as page elements like
tables, forms, headers, openings or footers; complex page layout or presence of
multiple pages.
To encourage progress on deeper and more complex Information Extraction (IE)
we introduce a new task (named Kleister) with two new datasets. Utilizing both
textual and structural layout features, an NLP system must find the most
important information, about various types of entities, in long formal
documents. We propose Pipeline method as a text-only baseline with different
Named Entity Recognition architectures (Flair, BERT, RoBERTa). Moreover, we
checked the most popular PDF processing tools for text extraction (pdf2djvu,
Tesseract and Textract) in order to analyze behavior of IE system in presence
of errors introduced by these tools.

本文提出 Kleister 任务以解决长正式文档的信息提取问题，考虑了文本和结构布局特征，并使用 Flair、BERT 和 RoBERTa 等具有不同实体识别体系结构的 pipeline 方法作为文本基准线，同时分析了 pdf2djvu、Tesseract 和 Textract 等常用 PDF 处理工具在 IE 系统中引入错误的行为。