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date: 24 June 2021

Abstract and Keywords

Automated text summarization systems seek to provide the most important content contained in their (single or multiple document, and static or streaming over time) input. Extractive summarizers use various methods to assign an importance score to each fragment of the input and return the highest-scoring fragments, while abstractive summarizers attempt to compress and reformulate the extracted fragments and regenerate them in original and more elegant and coherent form. Numerous methods of scoring, combination, and compression have been developed. Evaluating the linguistic quality of a summary is much easier than evaluating the adequacy of its content. Various methods have been developed to compare system summary contents to the summaries of humans.

Keywords: automated text summarization, extract[ive] summary, abstract, maximal marginal relevance, ROUGE evaluation, Pyramid Method, summary content unit

Eduard Hovy

Eduard Hovy is research full professor at the Language Technologies Institute in Carnegie Mellon University’s School of Computer Science. He also holds adjunct professorships in CMU’s Machine Learning Department and at USC (Los Angeles) and BUPT (China). At the present time he is a Program Manager at DARPA. Dr Hovy completed a Ph.D. in Computer Science (Artificial Intelligence) at Yale University in 1987, and was awarded honorary doctorates from the National Distance Education University in Madrid in 2013 and the University of Antwerp in 2015. He is one of the initial 17 Fellows of ACL and a Fellow of AAAI.

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