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

Fuente: arXiv

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Detalles Bibliográficos
Autores principales:	Li, Yiyang, Li, Lei, Hu, Dingxin, Hao, Xueyi, Litvak, Marina, Vanetik, Natalia, Zhou, Yanquan
Formato:	Preprint
Publicado:	2024
Materias:	Computation and Language
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

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author	Li, Yiyang Li, Lei Hu, Dingxin Hao, Xueyi Litvak, Marina Vanetik, Natalia Zhou, Yanquan
author_facet	Li, Yiyang Li, Lei Hu, Dingxin Hao, Xueyi Litvak, Marina Vanetik, Natalia Zhou, Yanquan
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2402_08581
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Improving Factual Error Correction for Abstractive Summarization via Data Distillation and Conditional-generation Cloze Li, Yiyang Li, Lei Hu, Dingxin Hao, Xueyi Litvak, Marina Vanetik, Natalia Zhou, Yanquan Computation and Language 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.
title	Improving Factual Error Correction for Abstractive Summarization via Data Distillation and Conditional-generation Cloze
topic	Computation and Language
url	https://arxiv.org/abs/2402.08581

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