@InProceedings{chachra-EtAl:2016:COLING,
  author    = {Chachra, Suchet  and  Ben Abacha, Asma  and  Shooshan, Sonya  and  Rodriguez, Laritza  and  Demner-Fushman, Dina},
  title     = {A Hybrid Approach to Generation of Missing Abstracts in Biomedical Literature},
  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     = {1093--1100},
  abstract  = {Readers usually rely on abstracts to identify relevant medical information from
	scientific articles. Abstracts are also essential to advanced information
	retrieval methods. More than 50 thousand scientific publications in PubMed lack
	author-generated abstracts, and the relevancy judgements for these papers have
	to be based on their titles alone. In this paper, we propose a hybrid
	summarization technique that aims to select the most pertinent sentences from
	articles to generate an extractive summary in lieu of a missing abstract. We
	combine i) health outcome detection, ii) keyphrase extraction, and iii) textual
	entailment recognition between sentences. We evaluate our hybrid approach and
	analyze the improvements of multi-factor summarization over techniques that
	rely on a single method, using a collection of 295 manually generated reference
	summaries. The obtained results show that the hybrid approach outperforms the
	baseline techniques with an improvement of 13% in recall and 4% in F1 score.},
  url       = {http://aclweb.org/anthology/C16-1104}
}