@InProceedings{wang-EtAl:2017:Long4,
  author    = {Wang, Liangguo  and  Jiang, Jing  and  Chieu, Hai Leong  and  Ong, Chen Hui  and  Song, Dandan  and  Liao, Lejian},
  title     = {Can Syntax Help? Improving an LSTM-based Sentence Compression Model for New Domains},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1385--1393},
  abstract  = {In this paper, we study how to improve the
	domain adaptability of a deletion-based
	Long Short-Term Memory (LSTM) neural network model for sentence compression. We
	hypothesize that syntactic information helps in making such models
	more robust across domains. We propose two major changes to the model: using
	explicit syntactic features and introducing syntactic constraints through
	Integer Linear Programming (ILP). Our evaluation
	shows that the proposed model works better than the original model as well as a
	traditional non-neural-network-based model
	in a cross-domain setting.},
  url       = {http://aclweb.org/anthology/P17-1127}
}