Abstract Automatic Text Summarization (ATS) is an important area in Natural Language
Processing (NLP) with the goal of shortening a long text into a more compact version by …

Text summarization automatically produces a summary containing important sentences and
includes all relevant important information from the original document. One of the main …

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 …

Deep neural networks (DNNs) have been recently found popular for image captioning
problems in remote sensing (RS). Existing DNN-based approaches rely on the availability of …

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 …

Text summarization is essential in this fast-growing world to read the information because a
vast amount of information holds various definitions among related contents. Due to this …

Financial news summarization (FNS) has been an attractive research problem in recent
years, which aims to generate a shorter highlight of the news article while preserving key …

Text summarization is the process of generating a brief version of a text that preserves the
salient information of the text. For information retrieval, it is a good dimension reduction …

Abstractive text summarization using attentional recurrent neural network (sequence-to-
sequence) models have proven to be very effective. In this paper, a novel hybrid approach is …

Abstractive text summarisation is essential to producing natural language summaries with
main ideas from large text documents. Despite the success of English language-based …