  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.
