State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China.
Guilin University of Electronic Technology, Guilin, China.
Comput Intell Neurosci. 2022 Aug 1;2022:7132226. doi: 10.1155/2022/7132226. eCollection 2022.
With the rapid development of the Internet, the massive amount of web textual data has grown exponentially, which has brought considerable challenges to downstream tasks, such as document management, text classification, and information retrieval. Automatic text summarization (ATS) is becoming an extremely important means to solve this problem. The core of ATS is to mine the gist of the original text and automatically generate a concise and readable summary. Recently, to better balance and develop these two aspects, deep learning (DL)-based abstractive summarization models have been developed. At present, for ATS tasks, almost all state-of-the-art (SOTA) models are based on DL architecture. However, a comprehensive literature survey is still lacking in the field of DL-based abstractive text summarization. To fill this gap, this paper provides researchers with a comprehensive survey of DL-based abstractive summarization. We first give an overview of abstractive summarization and DL. Then, we summarize several typical frameworks of abstractive summarization. After that, we also give a comparison of several popular datasets that are commonly used for training, validation, and testing. We further analyze the performance of several typical abstractive summarization systems on common datasets. Finally, we highlight some open challenges in the abstractive summarization task and outline some future research trends. We hope that these explorations will provide researchers with new insights into DL-based abstractive summarization.
随着互联网的飞速发展，网络文本数据呈指数级增长，这给文档管理、文本分类和信息检索等下游任务带来了相当大的挑战。自动文本摘要（ATS）正成为解决这一问题的重要手段。ATS 的核心是挖掘原文的要点，并自动生成简洁易读的摘要。最近，为了更好地平衡和发展这两个方面，基于深度学习（DL）的抽象摘要模型得到了发展。目前，几乎所有基于 ATS 任务的最先进（SOTA）模型都是基于 DL 架构的。然而，在基于 DL 的抽象文本摘要领域，仍然缺乏全面的文献调查。为了填补这一空白，本文为研究人员提供了基于 DL 的抽象摘要的全面调查。我们首先概述了抽象摘要和 DL。然后，我们总结了几种典型的抽象摘要框架。之后，我们还比较了几种常用于训练、验证和测试的流行数据集。我们进一步分析了几种典型的抽象摘要系统在常见数据集上的性能。最后，我们强调了抽象摘要任务中的一些开放挑战，并概述了一些未来的研究趋势。我们希望这些探索能为研究人员提供对基于 DL 的抽象摘要的新见解。
