This paper describes Macquarie University’s contribution to the BioASQ Challenge (BioASQ 6b, Phase B). We focused on the extraction of the ideal answers, and the task was approached as an instance of query-based multi-document summarisation. In particular, this paper focuses on the experiments related to the deep learning and reinforcement learning approaches used in the submitted runs. The best run used a deep learning model under a regression-based framework. The deep learning architecture used features derived from the output of LSTM chains on word embeddings, plus features based on similarity with the query, and sentence position. The reinforcement learning approach was a proof-of-concept prototype that trained a global policy using REINFORCE. The global policy was implemented as a neural network that used tf.idf features encoding the candidate sentence, question, and context.
|Title of host publication||The 6th BioASQ Workshop|
|Subtitle of host publication||A challenge on large-scale biomedical semantic indexing and question answering: Proceedings of the Workshop|
|Place of Publication||Stroudsburg|
|Publisher||Association for Computational Linguistics|
|Number of pages||8|
|Publication status||Published - 2018|
|Event||2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) - Brussels, Belgium|
Duration: 31 Oct 2018 → 4 Nov 2018
|Conference||2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)|
|Period||31/10/18 → 4/11/18|
Molla, D. (2018). Macquarie University at BioASQ 6b: deep learning and deep reinforcement learning for query-based multi-document summarisation. In The 6th BioASQ Workshop: A challenge on large-scale biomedical semantic indexing and question answering: Proceedings of the Workshop (pp. 22-29). Stroudsburg: Association for Computational Linguistics.