Macquarie University at BioASQ 5b -- Query-based Summarisation Techniques for Selecting the Ideal Answers
June 07, 2017 ยท Declared Dead ยท ๐ Workshop on Biomedical Natural Language Processing
"No code URL or promise found in abstract"
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Authors
Diego Molla-Aliod
arXiv ID
1706.02095
Category
cs.CL: Computation & Language
Citations
10
Venue
Workshop on Biomedical Natural Language Processing
Last Checked
5 months ago
Abstract
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.
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