Abstract Automatic Text Summarization (ATS) involves estimating the salience of information
and creating coherent summaries that include all relevant and important information from the …

In this paper, an abstractive Arabic text summarization model that is based on sequence-to-
sequence recurrent neural networks is proposed. It consists of a multilayer encoder and …

In the Web 3.0 platforms, enormous amount of information is shared whereby individuals
express their thoughts and opinions and learn from others' experiences. Many e-commerce …

Extractive summarization has recently gained significant attention as a classification
problem at the sentence level. Most current summarization methods rely on only one way of …

The recent advancements in big data and natural language processing (NLP) have
necessitated proficient text mining (TM) schemes that can interpret and analyze voluminous …

Fault diagnosis is of great significance for ensuring the safety and reliable operation of
rolling bearing in industries. Stack autoencoder (SAE) networks have been widely applied in …

Document Summarization is the procedure of generating a meaningful and concise
summary of a given document with the inclusion of relevant and topic-important points …

Text summarization generates a concise summary of the available information by
determining the most relevant and important sentences in the document. In this paper, an …

Due to a large influx of information around us, text summarization is becoming increasingly
important. Text summary helps one understand the document without having to read every …

Along with the extreme expansion of big data and the vast development of the internet,
making documentation of the huge internet information is the first interest for people. These …