Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query-based multi-document summarisation
Table 1: Value of n (the number of sentences returned as the ideal answer) for each question type.
['[EMPTY]', '[BOLD] Summary', '[BOLD] Factoid', '[BOLD] Yesno', '[BOLD] List']
In all of the experiments reported in this paper, the input text is the set of relevant snippets that are associated with the question. These snippets are pre-processed by splitting them into sentences. The sentences are then scored using the deep learning approaches described below. Then, after all candidate sentences are scored, the top n sentences are returned as the ideal answer. ’s framework.