TY - GEN
T1 - Macquarie University at BioASQ 5b
T2 - 16th SIGBioMed Workshop on Biomedical Natural Language Processing
AU - Molla, Diego
PY - 2017
Y1 - 2017
N2 - Macquarie University’s contribution to the BioASQ challenge (Task 5b Phase B) focused on the use of query-based extractive summarisation techniques for the generation of the ideal answers. Four runs were submitted, with approaches ranging from a trivial system that selected the first n snippets, to the use of deep learning approaches under a regression framework. Our experiments and the ROUGE results of the five test batches of BioASQ indicate surprisingly good results for the trivial approach. Overall, most of our runs on the first three test batches achieved the best ROUGE-SU4 results in the challenge.
AB - Macquarie University’s contribution to the BioASQ challenge (Task 5b Phase B) focused on the use of query-based extractive summarisation techniques for the generation of the ideal answers. Four runs were submitted, with approaches ranging from a trivial system that selected the first n snippets, to the use of deep learning approaches under a regression framework. Our experiments and the ROUGE results of the five test batches of BioASQ indicate surprisingly good results for the trivial approach. Overall, most of our runs on the first three test batches achieved the best ROUGE-SU4 results in the challenge.
UR - http://www.scopus.com/inward/record.url?scp=85122971109&partnerID=8YFLogxK
U2 - 10.18653/v1/W17-2308
DO - 10.18653/v1/W17-2308
M3 - Conference proceeding contribution
AN - SCOPUS:85122971109
T3 - BioNLP 2017 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 16th BioNLP Workshop
SP - 67
EP - 75
BT - BioNLP 2017
PB - Association for Computational Linguistics
Y2 - 4 August 2017 through 4 August 2017
ER -