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State-of-the-art abstractive summarization systems often generate \emph{hallucinations}; i.e., content that is not directly inferable from the source text.
On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
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ROUGE: A package for automatic evaluation of summaries
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Evaluating content selection in summarization: The pyramid method
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fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 2019
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Factual error correction for abstractive summarization models
Meng Cao, Yue Dong, Jiapeng Wu, and Jackie Chi Kit Cheung. 2020 · 2020
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FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization
Esin Durmus, He He, and Mona Diab. 2020 · 2020
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Controlled hallucinations: Learning to generate faithfully from noisy data
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Evaluating factuality in generation with dependency-level entailment
Asking and answering questions to evaluate the factual consistency of summaries
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On exposure bias, hallucination and domain shift in neural machine translation
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Understanding neural abstractive summarization models via uncertainty
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PEGASUS: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter Liu. 2020 · 2020
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Reducing quantity hallucinations in abstractive summarization
Zheng Zhao, Shay B. Cohen, and Bonnie Webber. 2020 · 2020
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Entity-level factual consistency of abstractive text summarization
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Planning with entity chains for abstractive summarization
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