  Text generation is increasingly common but often requires manual post-editing
where high precision is critical to end users. However, manual editing is
expensive so we want to ensure this effort is focused on high-value tasks. And
we want to maintain stylistic consistency, a particular challenge in crowd
settings. We present a case study, analysing human post-editing in the context
of a template-based biography generation system. An edit flow visualisation
combined with manual characterisation of edits helps identify and prioritise
work for improving end-to-end efficiency and accuracy.
