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 on-line renders automated understanding of long texts a critical area of research. This article has two goals: a) it overviews the relevant neural building blocks, thus serving as a short tutorial, and b) it surveys the state-of-the-art in long document NLP, mainly focusing on two central 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. Additionally, this article discusses the main challenges, issues and current solutions related to long document NLP. Finally, the relevant, publicly available, annotated datasets are presented, in order to facilitate further research.

翻译：深度神经网络（DNN）的采用在过去十年中极大地促进了自然语言处理（NLP）的发展。然而，长文档分析的需求与短文本分析截然不同，而在线上传文档的规模持续增长，使得自动理解长文本成为关键研究领域。本文有两个目标：a）概述相关的神经构建模块，从而作为简短的教程；b）综述长文档NLP的前沿技术，主要聚焦于两项核心任务：文档分类与文档摘要。长文本的情感分析也涵盖在内，因其通常被视为文档分类的特殊情况。此外，本文讨论了长文档NLP面临的主要挑战、问题及当前解决方案。最后，介绍了相关公开可用的标注数据集，以促进进一步研究。