TITLE: WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization 

ABSTRACT: We introduce WikiLingua, a large-scale, multilingual dataset for the
evaluation of crosslingual abstractive summarization systems. We extract
article and summary pairs in 18 languages from WikiHow, a high quality,
collaborative resource of how-to guides on a diverse set of topics written by
human authors. We create gold-standard article-summary alignments across
languages by aligning the images that are used to describe each how-to step in
an article. As a set of baselines for further studies, we evaluate the
performance of existing cross-lingual abstractive summarization methods on our
dataset. We further propose a method for direct crosslingual summarization
(i.e., without requiring translation at inference time) by leveraging synthetic
data and Neural Machine Translation as a pre-training step. Our method
significantly outperforms the baseline approaches, while being more cost
efficient during inference.
We introduce WikiLingua, a large-scale, multilingual dataset for the
evaluation of crosslingual abstractive summarization systems. We extract
article and summary pairs in 18 languages from WikiHow, a high quality,
collaborative resource of how-to guides on a diverse set of topics written by
human authors. We create gold-standard article-summary alignments across
languages by aligning the images that are used to describe each how-to step in
an article. As a set of baselines for further studies, we evaluate the
performance of existing cross-lingual abstractive summarization methods on our
dataset. We further propose a method for direct crosslingual summarization
(i.e., without requiring translation at inference time) by leveraging synthetic
data and Neural Machine Translation as a pre-training step. Our method
significantly outperforms the baseline approaches, while being more cost
efficient during inference.
WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization
http://arxiv.org/abs/2010.03093v1