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

• URL: http://arxiv.org/abs/2402.08581v1
• Date: Tue, 13 Feb 2024 16:35:48 GMT
• Title: Improving Factual Error Correction for Abstractive Summarization via Data Distillation and Conditional-generation Cloze
• Authors: Yiyang Li and Lei Li and Dingxin Hu and Xueyi Hao and Marina Litvak and Natalia Vanetik and Yanquan Zhou
• Abstract summary: We first propose a novel factual error correction model FactCloze based on a conditional-generation cloze task. We then propose a data distillation method to generate a more faithful summarization dataset SummDSC.
• Score: 11.589564922148913
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 be answered or not. Then, we propose a data distillation method to generate a more faithful summarization dataset SummDSC via multiple-dimensional evaluation. We experimentally validate the effectiveness of our approach, which leads to an improvement in multiple factual consistency metrics compared to baselines.

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