@InProceedings{nareshkumar-EtAl:2018:BioASQ,
  author    = {Naresh Kumar, Ashwin  and  Kesavamoorthy, Harini  and  Das, Madhura  and  Kalwad, Pramati  and  Chandu, Khyathi  and  Mitamura, Teruko  and  Nyberg, Eric},
  title     = {Ontology-Based Retrieval \& Neural Approaches for BioASQ Ideal Answer Generation},
  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     = {79--89},
  abstract  = {The ever-increasing magnitude of biomedical information sources makes it difficult and time-consuming for a human researcher to find the most relevant documents and pinpointed answers for a specific question or topic when using only a traditional search engine. Biomedical Question Answering systems automatically identify the most relevant documents and pinpointed answers, given an information need expressed as a natural language question. Generating a non-redundant, human-readable summary that satisfies the information need of a given biomedical question is focus of the Ideal Answer Generation task, part of the BioASQ challenge. This paper presents a system for ideal answer generation (using ontology-based retrieval and a neural learning-to-rank approach, combined with extractive and abstractive summarization techniques) which achieved the highest ROUGE score of 0.659 on the BioASQ 5b batch 2 test.},
  url       = {http://www.aclweb.org/anthology/W18-5310}
}