Abstract Automatic Text Summarization (ATS) is becoming much more important because of
the huge amount of textual content that grows exponentially on the Internet and the various …

The amount of text that is generated every day is increasing dramatically. This tremendous
volume of mostly unstructured text cannot be simply processed and perceived by computers …

Large language models (LLMs) have shown promise for automatic summarization but the
reasons behind their successes are poorly understood. By conducting a human evaluation …

Proposal of large-scale datasets has facilitated research on deep neural models for news
summarization. Deep learning can also be potentially useful for spoken dialogue …

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 …

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 …

We propose a text editor to help users plan, structure and reflect on their writing process. It
provides continuously updated paragraph-wise summaries as margin annotations, using …

The increasing availability of online information has triggered an intensive research in the
area of automatic text summarization within the Natural Language Processing (NLP). Text …

recently, after the life of the individual changed and became more crowded with all the
concerns of life, and with the diversity and the increasing of sources of knowledge on the …

In recent years, the volume of textual data has rapidly increased, which has generated a
valuable resource for extracting and analysing information. To retrieve useful knowledge …