论文标题
MFACE：多语言摘要和事实一致性评估
mFACE: Multilingual Summarization with Factual Consistency Evaluation
论文作者
论文摘要
近年来，由于预先培训的语言模型和大规模数据集的可用性，抽象性摘要近年来引起了人们的兴趣。尽管结果有希望，但当前的模型仍会产生实际不一致的摘要，从而减少了对现实应用程序的实用性。最近的一些努力通过设计模型来解决这一问题，这些模型自动检测到机器生成的摘要中的事实不一致。但是，他们专注于英语，这是一种具有丰富资源的语言。在这项工作中，我们利用事实一致性评估模型来改善多语言摘要。我们根据多语言NLI模型提供的信号，即数据过滤和受控生成，探索两种直观的方法来减轻幻觉。 XLSUM数据集的45种语言的实验结果显示出对自动和人类评估的强基础的增长。
Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets. Despite promising results, current models still suffer from generating factually inconsistent summaries, reducing their utility for real-world application. Several recent efforts attempt to address this by devising models that automatically detect factual inconsistencies in machine generated summaries. However, they focus exclusively on English, a language with abundant resources. In this work, we leverage factual consistency evaluation models to improve multilingual summarization. We explore two intuitive approaches to mitigate hallucinations based on the signal provided by a multilingual NLI model, namely data filtering and controlled generation. Experimental results in the 45 languages from the XLSum dataset show gains over strong baselines in both automatic and human evaluation.
