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The ability to convey relevant and faithful information is critical for many tasks in conditional generation and yet remains elusive for neural seq-to-seq models whose outputs often reveal hallucinations and fail to correctly cover important details.
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Learning to summarize with human feedback
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SummEval: Re-evaluating summarization evaluation
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Ranking generated summaries by correctness: An interesting but challenging application for natural language inference
Tobias Falke, Leonardo F. R. Ribeiro, Prasetya Ajie Utama, Ido Dagan, and Iryna Gurevych. 2019 · 2019
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Hayate Iso, Yui Uehara, Tatsuya Ishigaki, Hiroshi Noji, Eiji Aramaki, Ichiro Kobayashi, Yusuke Miyao, Naoaki Okazaki, and Hiroya Takamura. 2019 · 2019
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Natural questions: A benchmark for question answering research
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A survey of approaches to automatic question generation:from 2019 to early 2021
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HIBRIDS: Attention with hierarchical biases for structure-aware long document summarization
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