Abstract Automatic Text Summarization (ATS) is becoming much more important because of
the huge amount of textual content that grows exponentially on the Internet and the various …

As software vulnerabilities grow in volume and complexity, researchers proposed various
Artificial Intelligence (AI)-based approaches to help under-resourced security analysts to …

Neural network-based methods for abstractive summarization produce outputs that are more
fluent than other techniques, but which can be poor at content selection. This work proposes …

Neural sequence-to-sequence models have provided a viable new approach for abstractive
text summarization (meaning they are not restricted to simply selecting and rearranging …

We present deep communicating agents in an encoder-decoder architecture to address the
challenges of representing a long document for abstractive summarization. With deep …

Keyphrase provides highly-condensed information that can be effectively used for
understanding, organizing and retrieving text content. Though previous studies have …

In the past few years, neural abstractive text summarization with sequence-to-sequence
(seq2seq) models have gained a lot of popularity. Many interesting techniques have been …

We propose a selective encoding model to extend the sequence-to-sequence framework for
abstractive sentence summarization. It consists of a sentence encoder, a selective gate …

Recurrent neural network models with an attention mechanism have proven to be extremely
effective on a wide variety of sequence-to-sequence problems. However, the fact that soft …

Generating a text abstract from a set of documents remains a challenging task. The neural
encoder-decoder framework has recently been exploited to summarize single documents …