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The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey
[article]
2021
arXiv
pre-print
Recently, various neural encoder-decoder models pioneered by Seq2Seq framework have been proposed to achieve the goal of generating more abstractive summaries by learning to map input text to output text. At a high level, such neural models can freely generate summaries without any constraint on the words or phrases used. Moreover, their format is closer to human-edited summaries and output is more readable and fluent. However, the neural model's abstraction ability is a double-edged sword. A
arXiv:2104.14839v2
fatcat:37glddlmnbdnfnpk45jelcejuu