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

Kleister: A novel task for Information Extraction involving Long Documents with Complex Layout