Abstract Automatic Text Summarization (ATS) is an important area in Natural Language
Processing (NLP) with the goal of shortening a long text into a more compact version by …

The focus of automatic text summarization research has exhibited a gradual shift from
extractive methods to abstractive methods in recent years, owing in part to advances in …

We introduce extreme summarization, a new single-document summarization task which
does not favor extractive strategies and calls for an abstractive modeling approach. The idea …

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 …

Text summarization aims at compressing long documents into a shorter form that conveys
the most important parts of the original document. Despite increased interest in 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 …

Natural Language Processing (NLP) helps empower intelligent machines by enhancing a
better understanding of the human language for linguistic-based human-computer …

Single document summarization is the task of producing a shorter version of a document
while preserving its principal information content. In this paper we conceptualize extractive …

We present NEWSROOM, a summarization dataset of 1.3 million articles and summaries
written by authors and editors in newsrooms of 38 major news publications. Extracted from …

Recently BERT has been adopted for document encoding in state-of-the-art text
summarization models. However, sentence-based extractive models often result in …