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
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.findings-emnlp.360.pdf
- Code
- esdurmus/Wikilingua
- Data
- WikiLingua, Global Voices, WikiHow