Abstract
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.
Abstract (translated)
在过去的十年中，采用深度神经网络(DNNs)极大地促进了自然语言处理(NLP)的发展。然而，对长文档的分析需求与对短文本的分析需求 quite different，随着在线文档上传内容的日益增加，使得对长文档的NLP分析成为一个重要的研究领域。本文综述了该领域当前的研究进展，概述了相关的神经网络构建块，随后重点探讨了 two main NLP任务：文档分类、摘要提取以及在Sentiment Analysis中的具体应用。本文详细描述了与长文档NLP相关的挑战、问题和当前的解决方案。此外，我们还列出了目前公开可用、标签明确的长文档数据集。
URL
https://arxiv.org/abs/2305.16259
