Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query-based multi-document summarisation
This work addresses biomedical document summarization for researchers, but it is incremental as it applies existing deep learning and reinforcement learning methods to a specific benchmark task.
The paper tackled query-based multi-document summarization for extracting ideal answers in the BioASQ 6b challenge, achieving best results with a deep learning model using LSTM features, query similarity, and sentence position, while also developing a proof-of-concept reinforcement learning prototype.
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.