Training Dynamics for Text Summarization Models

ROUGE: A Package for Automatic Evaluation of Summaries

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MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance

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Evaluating factuality in generation with dependency-level entailment

Tanya Goyal and Greg Durrett. 2020 · 2020

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Evaluating the factual consistency of abstractive text summarization

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Towards Debiasing NLU Models from Unknown Biases

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Transformers: State-of-the-art natural language processing

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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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Towards question-answering as an automatic metric for evaluating the content quality of a summary

Daniel Deutsch, Tania Bedrax-Weiss, and Dan Roth. 2021 · 2021

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Annotating and modeling fine-grained factuality in summarization

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