Kleister: A novel task for Information Extraction involving Long Documents with Complex Layout
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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 real-world longer documents with information encoded in the spatial structure of the document, in elements like tables, forms, headers, openings or footers, or the complex layout of pages or multiple pages. To encourage progress on deeper and more complex information extraction, we present a new task (named Kleister) with two new datasets. Based on textual and structural layout features, an NLP system must find the most important information, about various types of entities, in formal long documents. These entities are not only classes from standard named entity recognition (NER) systems (e.g. location, date, or amount) but also the roles of the entities in the whole documents (e.g. company town address, report date, income amount).
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