With the rapid growth of social media platforms, digitization of official records, and digital
publication of articles, books, magazines, and newspapers, lots of data are generated every …

We provide a literature review about Automatic Text Summarization (ATS) systems. We
consider a citation-based approach. We start with some popular and well-known papers that …

The primary challenge faced in extractive text summarization is related to the scoring of
sentences, with the critical factor for scoring being the manner in which the sentence …

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 …

In the field of text summarization, extractive techniques aim to extract key sentences from a
document to form a summary. However, traditional methods are not sensitive enough to …

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

Automatic Text Summarization (ATS) is gaining popularity as there is a growing demand for
a system capable of processing extensive textual content and delivering a concise, yet …

In recent times, artificial intelligence (AI) methods have been applied in document and
content management to make decisions and improve the organization's functionalities …

Day by day, the amount of online and offline text data is growing tremendously from various
sources like legal documents, medical documents, news articles, etc. Manual text …

The keyphrases of a document are the textual units that characterize its content such as the
topics it addresses, its ideas, their field, etc. Thousands of books, articles and web pages are …