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        <title>Detecting (Un)Important Content for Single-Document News Summarization</title>
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    <abstract>We present a robust approach for detecting intrinsic sentence importance in news, by training on two corpora of document-summary pairs. When used for single-document summarization, our approach, combined with the “beginning of document” heuristic, outperforms a state-of-the-art summarizer and the beginning-of-article baseline in both automatic and manual evaluations. These results represent an important advance because in the absence of cross-document repetition, single document summarizers for news have not been able to consistently outperform the strong beginning-of-article baseline.</abstract>
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%0 Conference Proceedings
%T Detecting (Un)Important Content for Single-Document News Summarization
%A Yang, Yinfei
%A Bao, Forrest
%A Nenkova, Ani
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F yang-etal-2017-detecting
%X We present a robust approach for detecting intrinsic sentence importance in news, by training on two corpora of document-summary pairs. When used for single-document summarization, our approach, combined with the “beginning of document” heuristic, outperforms a state-of-the-art summarizer and the beginning-of-article baseline in both automatic and manual evaluations. These results represent an important advance because in the absence of cross-document repetition, single document summarizers for news have not been able to consistently outperform the strong beginning-of-article baseline.
%U https://aclanthology.org/E17-2112
%P 707-712

Markdown (Informal)

[Detecting (Un)Important Content for Single-Document News Summarization](https://aclanthology.org/E17-2112) (Yang et al., EACL 2017)

• Detecting (Un)Important Content for Single-Document News Summarization (Yang et al., EACL 2017)

ACL

• Yinfei Yang, Forrest Bao, and Ani Nenkova. 2017. Detecting (Un)Important Content for Single-Document News Summarization. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 707–712, Valencia, Spain. Association for Computational Linguistics.