Abstractive summarization of hospitalisation histories with transformer networks
In this paper we present a novel approach to abstractive summarization of patient hospitalisation histories. We applied an encoder-decoder framework with Longformer neural network as an encoder and BERT as a decoder. Our experiments show improved quality on some summarization tasks compared with pointer-generator networks. We also conducted a study with experienced physicians evaluating the results of our model in comparison with PGN baseline and human-generated abstracts, which showed the effectiveness of our model.
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Methods
Adam •
AdamW •
Attention Dropout •
BERT •
Dense Connections •
Dilated Sliding Window Attention •
Dropout •
GELU •
Global and Sliding Window Attention •
Layer Normalization •
Linear Layer •
Linear Warmup With Linear Decay •
Longformer •
Multi-Head Attention •
Residual Connection •
Scaled Dot-Product Attention •
Sliding Window Attention •
Softmax •
Weight Decay •
WordPiece
