The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

Numerous approaches for identifying important content for automatic text summarization
have been developed to date. Topic representation approaches first derive an intermediate …

With the rapid development and popularization of Internet and mobile communication
technologies, text data mining has attracted much attention. In particular, with the wide use …

It has now been 50 years since the publication of Luhn's seminal paper on automatic
summarization. During these years the practical need for automatic summarization has …

We introduce a stochastic graph-based method for computing relative importance of textual
units for Natural Language Processing. We test the technique on the problem of Text …

The past few years have witnessed increased interest in the potential use of wireless sensor
networks (WSNs) in applications such as disaster management, combat field …

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 …

We develop a Ranking framework upon Recursive Neural Networks (R2N2) to rank
sentences for multi-document summarization. It formulates the sentence ranking task as a …

Text summarization is compress the source text into a diminished version conserving its
information content and overall meaning. Because of the great amount of the information we …

Advanced data analytics is one of the most revolutionary technological developments in the
21st century, which enables the discovery of underlining trends via sophisticated …