  Producing a reduced version of a source text, as in generic or focused
summarization, inherently involves two distinct subtasks: deciding on targeted
content and generating a coherent text conveying it. While some popular
approaches address summarization as a single end-to-end task, prominent works
support decomposed modeling for individual subtasks. Further, semi-automated
text reduction is also very appealing, where users may identify targeted
content while models would generate a corresponding coherent summary.
  In this paper, we focus on the second subtask, of generating coherent text
given pre-selected content. Concretely, we formalize \textit{Controlled Text
Reduction} as a standalone task, whose input is a source text with marked spans
of targeted content ("highlighting"). A model then needs to generate a coherent
text that includes all and only the target information. We advocate the
potential of such models, both for modular fully-automatic summarization, as
well as for semi-automated human-in-the-loop use cases. Facilitating proper
research, we crowdsource high-quality dev and test datasets for the task.
Further, we automatically generate a larger "silver" training dataset from
available summarization benchmarks, leveraging a pretrained summary-source
alignment model. Finally, employing these datasets, we present a supervised
baseline model, showing promising results and insightful analyses.
