A Survey on Neural Abstractive Summarization Methods and Factual Consistency of Summarization
It provides a review for researchers in natural language processing, but is incremental as it synthesizes existing work without novel contributions.
This paper surveys neural abstractive summarization methods and examines the issue of factual consistency in generated summaries, without presenting new experimental results or numerical improvements.
Automatic summarization is the process of shortening a set of textual data computationally, to create a subset (a summary) that represents the most important pieces of information in the original text. Existing summarization methods can be roughly divided into two types: extractive and abstractive. An extractive summarizer explicitly selects text snippets (words, phrases, sentences, etc.) from the source document, while an abstractive summarizer generates novel text snippets to convey the most salient concepts prevalent in the source.