The adoption of Deep Neural Networks (DNNs) has greatly benefited Natural Language Processing (NLP) during the past decade. However, the demands of long documents analysis are quite different from those of shorter texts, with the ever increasing size of documents uploaded online rendering NLP on long documents a critical area of research. This paper surveys the current state-of-the-art in the domain, overviewing the relevant neural building blocks and subsequently focusing on two main NLP tasks: Document Classification, Summarization as well as mentioning uses in Sentiment Analysis. We detail the challenges, issues and current solutions related to long-document NLP. We also list publicly available, labelled, long-document datasets used in current research.

翻译：深度神经网络（DNNs）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，长文档分析与短文本分析的需求存在显著差异，随着在线文档规模的持续增长，面向长文档的NLP已成为一个关键研究领域。本文系统综述了该领域当前的前沿技术，概述了相关神经构建模块，并重点聚焦于两大NLP任务：文档分类与摘要生成，同时提及了在情感分析中的应用。我们详细阐述了长文档NLP面临的挑战、问题及现有解决方案，并汇总了当前研究中公开可用的标注长文档数据集。