In this work, we define a new style transfer task: perspective shift, which
reframes a dialogue from informal first person to a formal third person
rephrasing of the text. This task requires challenging coreference resolution,
emotion attribution, and interpretation of informal text. We explore several
baseline approaches and discuss further directions on this task when applied to
short dialogues. As a sample application, we demonstrate that applying
perspective shifting to a dialogue summarization dataset (SAMSum) substantially
improves the zero-shot performance of extractive news summarization models on
this data. Additionally, supervised extractive models perform better when
trained on perspective shifted data than on the original dialogues. We release
our code publicly.Comment: Findings of EMNLP 2022, 18 page