A quick introduction to single-document text summarization

Automatic text summarization is a challenging NLP task to provide a short and possibly accurate summary of a long text. While, with the growing amount of online content, the need for understanding and summarizing content is very high. In pure technological terms, the challenge for creating well formed summaries is huge and results are, most of the time, still far from being perfect (or human-level).

The first research work on automatic text summarization goes back to 50 years ago and various techniques. Since then, they have been used to extract relevant content from unstructured text.  

“The different dimensions of text summarization can be generally categorized based on its input type (single or multi document), purpose (generic, domain specific, or query-based) and output type (extractive or abstractive).”

A Review on Automatic Text Summarization Approaches, 2016.

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