@InProceedings{chesney-EtAl:2017:NLPmJ,
  author    = {Chesney, Sophie  and  Liakata, Maria  and  Poesio, Massimo  and  Purver, Matthew},
  title     = {Incongruent Headlines: Yet Another Way to Mislead Your Readers},
  booktitle = {Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {56--61},
  abstract  = {This paper discusses the problem of incongruent headlines: those which do not
	accurately represent the information contained in the article with which they
	occur. We emphasise that this phenomenon should be considered separately from
	recognised problematic headline types such as clickbait and sensationalism, 
	arguing that existing natural language processing (NLP) methods applied to
	these related concepts are not appropriate for the automatic detection of
	headline incongruence, as an analysis beyond stylistic traits is necessary. We
	therefore suggest a number of alternative methodologies that may be appropriate
	to the task at hand as a foundation for future work in this area. In addition,
	we provide an analysis of existing data sets which are related to this work,
	and motivate the need for a novel data set in this domain.},
  url       = {http://www.aclweb.org/anthology/W17-4210}
}