In recent years, there has been a explosion in the amount of text data from a variety of
sources. This volume of text is an invaluable source of information and knowledge which …

This paper contains a large literature review in the research field of Text Summarisation (TS)
based on Human Language Technologies (HLT). TS helps users manage the vast amount …

In this work, we model abstractive text summarization using Attentional Encoder-Decoder
Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two …

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 …

Abstract Abstractive Text Summarization (ATS), which is the task of constructing summary
sentences by merging facts from different source sentences and condensing them into a …

In this work, we propose a novel method for training neural networks to perform single-
document extractive summarization without heuristically-generated extractive labels. We call …

We develop and demonstrate automatic image description methods using a large captioned
photo collection. One contribution is our technique for the automatic collection of this new …

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

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 …

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 …