  The adoption of Deep Neural Networks (DNNs) has greatly benefited Natural
Language Processing (NLP) during the past decade. However, the demands of long
document analysis are quite different from those of shorter texts, while the
ever increasing size of documents uploaded online renders automated
understanding of lengthy texts a critical issue. Relevant applications include
automated Web mining, legal document review, medical records analysis,
financial reports analysis, contract management, environmental impact
assessment, news aggregation, etc. Despite the relatively recent development of
efficient algorithms for analyzing long documents, practical tools in this
field are currently flourishing. This article serves as an entry point into
this dynamic domain and aims to achieve two objectives. First of all, it
provides an introductory overview of the relevant neural building blocks,
serving as a concise tutorial for the field. Secondly, it offers a brief
examination of the current state-of-the-art in two key long document analysis
tasks: document classification and document summarization. Sentiment analysis
for long texts is also covered, since it is typically treated as a particular
case of document classification. Consequently, this article presents an
introductory exploration of document-level analysis, addressing the primary
challenges, concerns, and existing solutions. Finally, it offers a concise
definition of "long text/document", presents an original overarching taxonomy
of common deep neural methods for long document analysis and lists publicly
available annotated datasets that can facilitate further research in this area.
