The increase in online text generation by humans and machines needs automatic text
summarization systems. Recent research studies commonly use deep learning, besides …

Text summarization is an incipient practice for verdict out the summary of the text article. Text
summarization has grew so uses such as Due to the enormous aggregate of information …

To facilitate the task of reading and searching information, it became necessary to find a way
to reduce the size of documents without affecting the content. The solution is in Automatic …

Many previous research studies on extractive text summarization consider a subset of words
in a document as keywords and use a sentence ranking function that ranks sentences based …

Multi-document summarization (MDS) is becoming a crucial task in natural language
processing. MDS targets to condense the most important information from a set of …

This paper describes an approach to generic Bengali text summarization using latent
semantic analysis (LSA). Our proposed LSA based single document summarization method …

There are multitudinous Indian languages coming up on the web each day. These
languages are basically providing information to the people in their regional languages. This …

We suggest a new method for the task of extractive text summarization using graph-based
ranking algorithms. The main idea of this paper is to rank Maximal Frequent Sequences …

Abstract The methods of Automatic Extractive Summarization (AES) uses the features of the
sentences of the original text to extract the most important information that will be considered …

In this paper, we revisit the challenging problem of unsupervised single-document
summarization and study the following aspects: Integer linear programming (ILP) based …