  Summaries of medical text shall be faithful by being consistent and factual
with source inputs, which is an important but understudied topic for safety and
efficiency in healthcare. In this paper, we investigate and improve
faithfulness in summarization on a broad range of medical summarization tasks.
Our investigation reveals that current summarization models often produce
unfaithful outputs for medical input text. We then introduce FaMeSumm, a
framework to improve faithfulness by fine-tuning pre-trained language models
based on medical knowledge. FaMeSumm performs contrastive learning on designed
sets of faithful and unfaithful summaries, and it incorporates medical terms
and their contexts to encourage faithful generation of medical terms. We
conduct comprehensive experiments on three datasets in two languages: health
question and radiology report summarization datasets in English, and a
patient-doctor dialogue dataset in Chinese. Results demonstrate that FaMeSumm
is flexible and effective by delivering consistent improvements over mainstream
language models such as BART, T5, mT5, and PEGASUS, yielding state-of-the-art
performances on metrics for faithfulness and general quality. Human evaluation
by doctors also shows that FaMeSumm generates more faithful outputs. Our code
is available at https://github.com/psunlpgroup/FaMeSumm .
