  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.

Computation and Language