@InProceedings{li-EtAl:2016:COLING2,
  author    = {Li, Chen  and  Wei, Zhongyu  and  Liu, Yang  and  Jin, Yang  and  Huang, Fei},
  title     = {Using Relevant Public Posts to Enhance News Article Summarization},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {557--566},
  abstract  = {A news article summary usually consists of 2-3 key sentences that reflect the
	gist of that news article. In this paper we explore using public posts
	following a new article to improve automatic summary generation for the news
	article. We propose different approaches to incorporate information from public
	posts, including using frequency information from the posts to re-estimate
	bigram weights in the ILP-based summarization model and to re-weight a
	dependency tree edge's importance for sentence compression, directly selecting
	sentences from posts as the final summary, and finally a strategy to combine
	the summarization results generated from news articles and posts. Our
	experiments on data collected from Facebook show that relevant public posts
	provide useful information and can be effectively leveraged to improve news
	article summarization results.},
  url       = {http://aclweb.org/anthology/C16-1054}
}