Query Focused Multi-document Summarisation of Biomedical Texts
This work addresses biomedical information retrieval for researchers, but is incremental as it applies existing methods to a specific challenge task.
The authors tackled query-focused multi-document summarization for biomedical texts in the BioASQ8b challenge, achieving their best results with a BERT-LSTM model that did not benefit from BioBERT or Siamese architectures.
This paper presents the participation of Macquarie University and the Australian National University for Task B Phase B of the 2020 BioASQ Challenge (BioASQ8b). Our overall framework implements Query focused multi-document extractive summarisation by applying either a classification or a regression layer to the candidate sentence embeddings and to the comparison between the question and sentence embeddings. We experiment with variants using BERT and BioBERT, Siamese architectures, and reinforcement learning. We observe the best results when BERT is used to obtain the word embeddings, followed by an LSTM layer to obtain sentence embeddings. Variants using Siamese architectures or BioBERT did not improve the results.