@InProceedings{li-EtAl:2018:BioASQ,
  author    = {Li, Yutong  and  Gekakis, Nicholas  and  Wu, Qiuze  and  Li, Boyue  and  Chandu, Khyathi  and  Nyberg, Eric},
  title     = {Extraction Meets Abstraction: Ideal Answer Generation for Biomedical Questions},
  booktitle = {Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering},
  month     = {November},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {57--65},
  abstract  = {The growing number of biomedical publications is a challenge for human researchers, who invest considerable effort to search for relevant documents and pinpointed answers. Biomedical Question Answering can automatically generate answers for a user's topic or question, significantly reducing the effort required to locate the most relevant information in a large document corpus. Extractive summarization techniques, which concatenate the most relevant text units drawn from multiple documents, perform well on automatic evaluation metrics like ROUGE, but score poorly on human readability, due to the presence of redundant text and grammatical errors in the answer. },
  url       = {http://www.aclweb.org/anthology/W18-5307}
}