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Automatic text summarization: A comprehensive survey
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 huge amount of textual content that grows exponentially on the Internet and the various …
Deep learning based abstractive text summarization: approaches, datasets, evaluation measures, and challenges
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 …
valuable resource for extracting and analysing information. To retrieve useful knowledge …
[HTML][HTML] FuzzyTP-BERT: Enhancing extractive text summarization with fuzzy topic modeling and transformer networks
In the rapidly evolving field of natural language processing, the demand for efficient
automated text summarization systems that not only distill extensive documents but also …
automated text summarization systems that not only distill extensive documents but also …
A systematic literature review of deep learning-based text summarization: Techniques, input representation, training strategies, mechanisms, datasets, evaluation, and …
Abstract Automatic Text Summarization (ATS) involves estimating the salience of information
and creating coherent summaries that include all relevant and important information from the …
and creating coherent summaries that include all relevant and important information from the …
Exploring the landscape of automatic text summarization: a comprehensive survey
The discipline of Automatic Text Summarization (ATS), which is expanding quickly, intends
to automatically create summaries of enormous amounts of text so that readers can save …
to automatically create summaries of enormous amounts of text so that readers can save …
[PDF][PDF] A hybrid approach for text summarization using semantic latent Dirichlet allocation and sentence concept map** with transformer
Automatic text summarization generates a summary that contains sentences reflecting the
essential and relevant information of the original documents. Extractive summarization …
essential and relevant information of the original documents. Extractive summarization …
Rouge metric evaluation for text summarization techniques
Abstract Approaches to Automatic Text Summarization try to extract key information from one
or more input texts and generate summaries whilst preserving content meaning. These …
or more input texts and generate summaries whilst preserving content meaning. These …
Multilayer encoder and single-layer decoder for abstractive Arabic text summarization
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 …
sequence recurrent neural networks is proposed. It consists of a multilayer encoder and …
[HTML][HTML] An optimized hybrid deep learning model based on word embeddings and statistical features for extractive summarization
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 …
problem at the sentence level. Most current summarization methods rely on only one way of …
[Retracted] N‐GPETS: Neural Attention Graph‐Based Pretrained Statistical Model for Extractive Text Summarization
The extractive summarization approach involves selecting the source document's salient
sentences to build a summary. One of the most important aspects of extractive …
sentences to build a summary. One of the most important aspects of extractive …