  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.
