  While there has been recent progress in abstractive summarization as applied
to different domains including news articles, scientific articles, and blog
posts, the application of these techniques to clinical text summarization has
been limited. This is primarily due to the lack of large-scale training data
and the messy/unstructured nature of clinical notes as opposed to other domains
where massive training data come in structured or semi-structured form.
Further, one of the least explored and critical components of clinical text
summarization is factual accuracy of clinical summaries. This is specifically
crucial in the healthcare domain, cardiology in particular, where an accurate
summary generation that preserves the facts in the source notes is critical to
the well-being of a patient. In this study, we propose a framework for
improving the factual accuracy of abstractive summarization of clinical text
using knowledge-guided multi-objective optimization. We propose to jointly
optimize three cost functions in our proposed architecture during training:
generative loss, entity loss and knowledge loss and evaluate the proposed
architecture on 1) clinical notes of patients with heart failure (HF), which we
collect for this study; and 2) two benchmark datasets, Indiana University Chest
X-ray collection (IU X-Ray), and MIMIC-CXR, that are publicly available. We
experiment with three transformer encoder-decoder architectures and demonstrate
that optimizing different loss functions leads to improved performance in terms
of entity-level factual accuracy.
