This paper reviews research on automatic summarising in the last decade. This work has
grown, stimulated by technology and by evaluation programmes. The paper uses several …

This article describes research and development on the automated creation of summaries of
one or more texts. It defines the concept of summary and presents an overview of the …

Inspired by how humans summarize long documents, we propose an accurate and fast
summarization model that first selects salient sentences and then rewrites them abstractively …

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 …

Abstract Abstractive Sentence Summarization generates a shorter version of a given
sentence while attempting to preserve its meaning. We introduce a conditional recurrent …

Traditional approaches to extractive summarization rely heavily on human-engineered
features. In this work we propose a data-driven approach based on neural networks and …

Unlike extractive summarization, abstractive summarization has to fuse different parts of the
source text, which inclines to create fake facts. Our preliminary study reveals nearly 30% of …

Descriptive names are a vital part of readable, and hence maintainable, code. Recent
progress on automatically suggesting names for local variables tantalizes with the prospect …

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 …

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 …