Abstractive Summarization: A Survey of the State of the Art

  • Hui Lin University of Texas at Dallas
  • Vincent Ng University of Texas at Dallas

Abstract

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.

Published
2019-07-17
Section
Senior Member Presentation Track: Summary Talks