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

• URL: http://arxiv.org/abs/2010.03093v1
• Date: Wed, 7 Oct 2020 00:28:05 GMT
• Title: WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization
• Authors: Faisal Ladhak, Esin Durmus, Claire Cardie, Kathleen McKeown
• Abstract summary: 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.
• Score: 41.578594261746055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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