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In this work, we define a new style transfer task: perspective shift, whichreframes a dialogue from informal first person to a formal third personrephrasing of the text. This task requires challenging coreference resolution,emotion attribution, and interpretation of informal text. We explore severalbaseline approaches and discuss further directions on this task when applied toshort dialogues. As a sample application, we demonstrate that applyingperspective shifting to a dialogue summarization dataset (SAMSum) substantiallyimproves the zero-shot performance of extractive news summarization models onthis data. Additionally, supervised extractive models perform better whentrained on perspective shifted data than on the original dialogues. We releaseour code publicly.

He Said, She Said: Style Transfer for Shifting the Perspective of Dialogues