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Abstractive Summarization: A Survey of the State of the Art
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
The focus of automatic text summarization research has exhibited a gradual shift from extractive methods to abstractive methods in recent years, owing in part to advances in neural methods. Originally developed for machine translation, neural methods provide a viable framework for obtaining an abstract representation of the meaning of an input text and generating informative, fluent, and human-like summaries. This paper surveys existing approaches to abstractive summarization, focusing on the recently developed neural approaches.
doi:10.1609/aaai.v33i01.33019815
fatcat:ufoyvxeuundelfoul5kzbjeutq