A Survey on Neural Network-Based Summarization Methods
It provides a comprehensive review for researchers in natural language processing, but it is incremental as it synthesizes existing work without introducing new methods or results.
This paper surveys recent neural network-based methods for automatic text summarization, examining ten state-of-the-art models and discussing related techniques and future research directions.
Automatic text summarization, the automated process of shortening a text while reserving the main ideas of the document(s), is a critical research area in natural language processing. The aim of this literature review is to survey the recent work on neural-based models in automatic text summarization. We examine in detail ten state-of-the-art neural-based summarizers: five abstractive models and five extractive models. In addition, we discuss the related techniques that can be applied to the summarization tasks and present promising paths for future research in neural-based summarization.