The explosion of online and offline data has changed how we gather, evaluate, and
understand data. It is frequently difficult and time-consuming to comprehend large text …

Social media is used to categorise products or services, but analysing vast comments is time-
consuming. Researchers use sentiment analysis via natural language processing …

Recently, the Transformer model architecture and the pre-trained Transformer-based
language models have shown impressive performance when used in solving both natural …

Large text documents are sometimes challenging to understand and time-consuming to
extract vital information from. These issues are addressed by automatic text summarizing …

Text classification has critical applications, including healthcare, where it can assist patients
in locating specialized clinics based on their symptom descriptions. This can enhance …

The enormous quantity of text makes it challenging for users to obtain the key information
and knowledge. Automatic text summarization can alleviate this problem by providing …

Abstract In Natural Language Processing (NLP) domain, the majority of automatic text
summarization approaches depend on a prior knowledge of the language and/or the …

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

The significance of automatic text summarization (ATS) lies in its task of distilling textual
information into a condensed yet meaningful structure that preserves the core message of …

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