Summarization Models

AbstractMuch research has reported the training data of summarization models are noisy; summaries often do not reflect what is written in the source texts. We propose an effective method of …...

AbstractState-of-the-art summarization models still struggle to be factually consistent with the input text. A model-agnostic way to address this problem is post-editing the generated summaries. However, existing approaches typically fail …...

AbstractSummarization models often generate text that is poorly calibrated to quality metrics because they are trained to maximize the likelihood of a single reference (MLE). To address this, recent work …...