Papers › Query Focused Multi-document Summarisation of Biomedical Texts

Query Focused Multi-document Summarisation of Biomedical Texts

27 Aug 2020arXiv:2008.11986archive 2025-07-28

Diego Molla, Christopher Jones, Vincent Nguyen

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.

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Tasks

Reinforcement Learning (RL)Sentence Sentence Embeddings Word Embeddings regression reinforcement-learning

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Adam Attention Attention Dropout BERT Dense Connections Dropout LSTM Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Sigmoid Activation Softmax Tanh Activation Weight Decay WordPiece

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