@InProceedings{yang-bao-nenkova:2017:EACLshort,
  author    = {Yang, Yinfei  and  Bao, Forrest  and  Nenkova, Ani},
  title     = {Detecting (Un)Important Content for Single-Document News Summarization},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {707--712},
  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.},
  url       = {http://www.aclweb.org/anthology/E17-2112}
}