@inproceedings{ouyang-etal-2019-robust,
title = "A Robust Abstractive System for Cross-Lingual Summarization",
author = "Ouyang, Jessica and
Song, Boya and
McKeown, Kathy",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1204",
doi = "10.18653/v1/N19-1204",
pages = "2025--2031",
abstract = "We present a robust neural abstractive summarization system for cross-lingual summarization. We construct summarization corpora for documents automatically translated from three low-resource languages, Somali, Swahili, and Tagalog, using machine translation and the New York Times summarization corpus. We train three language-specific abstractive summarizers and evaluate on documents originally written in the source languages, as well as on a fourth, unseen language: Arabic. Our systems achieve significantly higher fluency than a standard copy-attention summarizer on automatically translated input documents, as well as comparable content selection.",
}
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<abstract>We present a robust neural abstractive summarization system for cross-lingual summarization. We construct summarization corpora for documents automatically translated from three low-resource languages, Somali, Swahili, and Tagalog, using machine translation and the New York Times summarization corpus. We train three language-specific abstractive summarizers and evaluate on documents originally written in the source languages, as well as on a fourth, unseen language: Arabic. Our systems achieve significantly higher fluency than a standard copy-attention summarizer on automatically translated input documents, as well as comparable content selection.</abstract>
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%0 Conference Proceedings
%T A Robust Abstractive System for Cross-Lingual Summarization
%A Ouyang, Jessica
%A Song, Boya
%A McKeown, Kathy
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F ouyang-etal-2019-robust
%X We present a robust neural abstractive summarization system for cross-lingual summarization. We construct summarization corpora for documents automatically translated from three low-resource languages, Somali, Swahili, and Tagalog, using machine translation and the New York Times summarization corpus. We train three language-specific abstractive summarizers and evaluate on documents originally written in the source languages, as well as on a fourth, unseen language: Arabic. Our systems achieve significantly higher fluency than a standard copy-attention summarizer on automatically translated input documents, as well as comparable content selection.
%R 10.18653/v1/N19-1204
%U https://aclanthology.org/N19-1204
%U https://doi.org/10.18653/v1/N19-1204
%P 2025-2031
Markdown (Informal)
[A Robust Abstractive System for Cross-Lingual Summarization](https://aclanthology.org/N19-1204) (Ouyang et al., NAACL 2019)
ACL
- Jessica Ouyang, Boya Song, and Kathy McKeown. 2019. A Robust Abstractive System for Cross-Lingual Summarization. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2025–2031, Minneapolis, Minnesota. Association for Computational Linguistics.