  In recent years, deep learning has revolutionized natural language processing
(NLP) by enabling the development of models that can learn complex
representations of language data, leading to significant improvements in
performance across a wide range of NLP tasks. Deep learning models for NLP
typically use large amounts of data to train deep neural networks, allowing
them to learn the patterns and relationships in language data. This is in
contrast to traditional NLP approaches, which rely on hand-engineered features
and rules to perform NLP tasks. The ability of deep neural networks to learn
hierarchical representations of language data, handle variable-length input
sequences, and perform well on large datasets makes them well-suited for NLP
applications. Driven by the exponential growth of textual data and the
increasing demand for condensed, coherent, and informative summaries, text
summarization has been a critical research area in the field of NLP. Applying
deep learning to text summarization refers to the use of deep neural networks
to perform text summarization tasks. In this survey, we begin with a review of
fashionable text summarization tasks in recent years, including extractive,
abstractive, multi-document, and so on. Next, we discuss most deep
learning-based models and their experimental results on these tasks. The paper
also covers datasets and data representation for summarization tasks. Finally,
we delve into the opportunities and challenges associated with summarization
tasks and their corresponding methodologies, aiming to inspire future research
efforts to advance the field further. A goal of our survey is to explain how
these methods differ in their requirements as understanding them is essential
for choosing a technique suited for a specific setting.

Computation and Language;Artificial Intelligence;Machine Learning