Abstractive Summarization: A Survey of the State of the Art
Year: 2019
Type: conference-abstract
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.
Authors: Hui Lin, Vincent Ng
Institution: The University of Texas at Dallas
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: National Science Foundation
OpenAlex ID: W2964813902