Abstractive summarization of hospitalisation histories with transformer networks
This addresses the problem of summarizing medical records for healthcare professionals, but it is incremental as it builds on existing transformer methods.
The paper tackled abstractive summarization of patient hospitalization histories using a Longformer encoder and BERT decoder, showing improved quality over pointer-generator networks in some tasks and effectiveness in physician evaluations.
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.