In recent years, there has been a explosion in the amount of text data from a variety of
sources. This volume of text is an invaluable source of information and knowledge which …

The focus of automatic text summarization research has exhibited a gradual shift from
extractive methods to abstractive methods in recent years, owing in part to advances in …

Neural sequence-to-sequence models have provided a viable new approach for abstractive
text summarization (meaning they are not restricted to simply selecting and rearranging …

With the evolution of the Internet and multimedia technology, the amount of text data has
increased exponentially. This text volume is a precious source of information and knowledge …

In the past few years, neural abstractive text summarization with sequence-to-sequence
(seq2seq) models have gained a lot of popularity. Many interesting techniques have been …

Despite the remarkable performance of generative large language models (LLMs) on
abstractive summarization, they face two significant challenges: their considerable size and …

The textual similarity is a crucial aspect for many extractive text summarization methods. A
bag-of-words representation does not allow to grasp the semantic relationships between …

The scientific research process generally starts with the examination of the state of the art,
which may involve a vast number of publications. Automatically summarizing scientific …

Neural abstractive summarization has been increasingly studied, where the prior work
mainly focused on summarizing single-speaker documents (news, scientific publications …

Text summarization is a subtask of natural language processing referring to the automatic
creation of a concise and fluent summary that captures the main ideas and topics from one …