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Most existing stylistic text rewriting methods and evaluation metrics operateon a sentence level, but ignoring the broader context of the text can lead topreferring generic, ambiguous, and incoherent rewrites. In this paper, weinvestigate integrating the preceding textual context into both the$\textit{rewriting}$ and $\textit{evaluation}$ stages of stylistic textrewriting, and introduce a new composite contextual evaluation metric$\texttt{CtxSimFit}$ that combines similarity to the original sentence withcontextual cohesiveness. We comparatively evaluate non-contextual andcontextual rewrites in formality, toxicity, and sentiment transfer tasks. Ourexperiments show that humans significantly prefer contextual rewrites as morefitting and natural over non-contextual ones, yet existing sentence-levelautomatic metrics (e.g., ROUGE, SBERT) correlate poorly with human preferences($\rho$=0--0.3). In contrast, human preferences are much better reflected byboth our novel $\texttt{CtxSimFit}$ ($\rho$=0.7--0.9) as well as proposedcontext-infused versions of common metrics ($\rho$=0.4--0.7). Overall, ourfindings highlight the importance of integrating context into the generationand especially the evaluation stages of stylistic text rewriting.

Don't Take This Out of Context! On the Need for Contextual Models and Evaluations for Stylistic Rewriting