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 a process of extracting salient information from a source text and
presenting that information to the user in a condensed form while preserving its main …

With the tremendous growth in the number of electronic documents, it is becoming
challenging to manage the volume of information. Much research has focused on …

Automatic text summarization plays a vital role in text retrieval. However, very little interest,
as well as attention to Hindi text summarization and the wide use of hybridization of an …

In paper, we propose an unsupervised text summarization model which generates a
summary by extracting salient sentences in given document (s). In particular, we model text …

Automatic text summarization methods are increasingly needed nowadays. Extractive multi-
document summarization approaches aim to obtain the main content of a document …

This paper proposes an optimization-based model for generic document summarization.
The model generates a summary by extracting salient sentences from documents. This …

Most of existing text automatic summarization algorithms are targeted for multi-documents of
relatively short length, thus difficult to be applied immediately to novel documents of …

Due to the tremendous increment of data on the web, extracting the most important data as a
conceptual brief would be valuable for certain users. Therefore, there is a massive …

The optimization of task scheduling process in the cloud-computing environment is the multi-
criteria NP-hard problem. The paper proposes a PSO based αPSO-TBLB (Task Based Load …