  Public trust in science depends on honest and factual communication of
scientific papers. However, recent studies have demonstrated a tendency of news
media to misrepresent scientific papers by exaggerating their findings. Given
this, we present a formalization of and study into the problem of exaggeration
detection in science communication. While there are an abundance of scientific
papers and popular media articles written about them, very rarely do the
articles include a direct link to the original paper, making data collection
challenging. We address this by curating a set of labeled press
release/abstract pairs from existing expert annotated studies on exaggeration
in press releases of scientific papers suitable for benchmarking the
performance of machine learning models on the task. Using limited data from
this and previous studies on exaggeration detection in science, we introduce
MT-PET, a multi-task version of Pattern Exploiting Training (PET), which
leverages knowledge from complementary cloze-style QA tasks to improve few-shot
learning. We demonstrate that MT-PET outperforms PET and supervised learning
both when data is limited, as well as when there is an abundance of data for
the main task.
