@InProceedings{wang-EtAl:2017:I17-2,
  author    = {Wang, Jinpeng  and  Hou, Yutai  and  Liu, Jing  and  Cao, Yunbo  and  Lin, Chin-Yew},
  title     = {A Statistical Framework for Product Description Generation},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {187--192},
  abstract  = {We present in this paper a statistical framework that generates accurate and
	fluent product description from product attributes. Specifically, after
	extracting templates and learning writing knowledge from attribute-description
	parallel data, we use the learned knowledge to decide what to say and how to
	say for product description generation. To evaluate accuracy and fluency for
	the generated descriptions, in addition to BLEU and Recall, we propose to
	measure what to say (in terms of attribute coverage) and to measure how to say
	(by attribute-specified generation) separately. Experimental results show that
	our framework is effective.},
  url       = {http://www.aclweb.org/anthology/I17-2032}
}