  Disinformation is often presented in long textual articles, especially when
it relates to domains such as health, often seen in relation to COVID-19. These
articles are typically observed to have a number of trustworthy sentences among
which core disinformation sentences are scattered. In this paper, we propose a
novel unsupervised task of identifying sentences containing key disinformation
within a document that is known to be untrustworthy. We design a three-phase
statistical NLP solution for the task which starts with embedding sentences
within a bespoke feature space designed for the task. Sentences represented
using those features are then clustered, following which the key sentences are
identified through proximity scoring. We also curate a new dataset with
sentence level disinformation scorings to aid evaluation for this task; the
dataset is being made publicly available to facilitate further research. Based
on a comprehensive empirical evaluation against techniques from related tasks
such as claim detection and summarization, as well as against simplified
variants of our proposed approach, we illustrate that our method is able to
identify core disinformation effectively.
