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

Faisal Ladhak, Esin Durmus, Claire Cardie, Kathleen McKeown


Abstract
We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of cross-lingual 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 cross-lingual 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.
Anthology ID:
2020.findings-emnlp.360
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4034–4048
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.360
DOI:
10.18653/v1/2020.findings-emnlp.360
Bibkey:
Cite (ACL):
Faisal Ladhak, Esin Durmus, Claire Cardie, and Kathleen McKeown. 2020. WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4034–4048, Online. Association for Computational Linguistics.
Cite (Informal):
WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization (Ladhak et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.360.pdf
Code
 esdurmus/Wikilingua
Data
WikiLinguaGlobal VoicesWikiHow