Improving Factual Error Correction for Abstractive Summarization via Data Distillation and Conditional-generation Cloze

Improving factual consistency in abstractive summarization has been a focus of current research. One promising approach is the post-editing method. However, previous works have yet to make sufficient use of factual factors in summaries and suffers from the negative effect of the training datasets. In this paper, we first propose a novel factual error correction model FactCloze based on a conditional-generation cloze task. FactCloze can construct the causality among factual factors while being able to determine whether the blank can …

cs.cl current data datasets distillation editing error error correction focus negative paper research summarization training via