Token-Level Fact Correction in Abstractive Summarization

This paper addresses fact correction for abstractive summarization of which aim is to edit a system-generated summary into a new source-consistent summary. The summaries generated by abstractive summarization models often contain various kinds of factual errors. Thus, fact correction becomes essenti...

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Bibliographic Details
Main Authors: Jeongwan Shin, Seong-Bae Park, Hyun-Je Song
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10005108/