Summarization, is to reduce the size of the document while preserving the meaning, is one
of the most researched areas among the Natural Language Processing (NLP) community …

Text summarization is the process of condensing a long text into a shorter version by
maintaining the key information and its meaning. Automatic text summarization can save …

As information is available in abundance for every topic on internet, condensing the
important information in the form of summary would benefit a number of users. Hence, there …

With the evolution of the Internet and multimedia technology, the amount of text data has
increased exponentially. This text volume is a precious source of information and knowledge …

Abstract Automatic Text Summarization (ATS) is an essential field in natural language
processing that attempts to condense large text documents so that users can assimilate …

Text summarization (TS) is considered one of the most difficult tasks in natural language
processing (NLP). It is one of the most important challenges that stand against the modern …

We propose a novel methodology for extractive, generic summarization of text documents.
The Maximum Independent Set, which has not been used previously in any summarization …

Text Summarization is the task of extracting salient information from the original text
document. In this process, the extracted information is generated as a condensed report and …

We propose a framework for abstractive summarization of multi-documents, which aims to
select contents of summary not from the source document sentences but from the semantic …

Nowadays, most research conducted in the field of abstractive text summarization focuses
on neural-based models alone, without considering their combination with knowledge …