@InProceedings{schulze-neves:2016:BioTxtM2016,
  author    = {Schulze, Frederik  and  Neves, Mariana},
  title     = {Entity-Supported Summarization of Biomedical Abstracts},
  booktitle = {Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {40--49},
  abstract  = {The increasing amount of biomedical information that is available for
	researchers and clinicians makes it harder to quickly find the right
	information. Automatic summarization of multiple texts can provide summaries
	specific to the user’s information needs. In this paper we look into the use
	named-entity recognition for graph-based summarization. We extend the LexRank
	algorithm with information about named entities and present EntityRank, a
	multi-document graph-based summarization algorithm that is solely based on
	named entities. We evaluate our system on a datasets of 1009 human written
	summaries provided by BioASQ and on 1974 gene summaries, fetched from the
	Entrez Gene database. The results show that the addition of named-entity
	information increases the performance of graph-based summarizers and that the
	EntityRank significantly outperforms the other methods with regard to the ROUGE
	measures.},
  url       = {http://aclweb.org/anthology/W16-5105}
}