  Automatic summarization of natural language is a current topic in computer
science research and industry, studied for decades because of its usefulness
across multiple domains. For example, summarization is necessary to create
reviews such as this one. Research and applications have achieved some success
in extractive summarization (where key sentences are curated), however,
abstractive summarization (synthesis and re-stating) is a hard problem and
generally unsolved in computer science. This literature review contrasts
historical progress up through current state of the art, comparing dimensions
such as: extractive vs. abstractive, supervised vs. unsupervised, NLP (Natural
Language Processing) vs Knowledge-based, deep learning vs algorithms,
structured vs. unstructured sources, and measurement metrics such as Rouge and
BLEU. Multiple dimensions are contrasted since current research uses
combinations of approaches as seen in the review matrix. Throughout this
summary, synthesis and critique is provided. This review concludes with
insights for improved abstractive summarization measurement, with surprising
implications for detecting understanding and comprehension in general.
