  Grounded text generation, encompassing tasks such as long-form
question-answering and summarization, necessitates both content selection and
content consolidation. Current end-to-end methods are difficult to control and
interpret due to their opaqueness. Accordingly, recent works have proposed a
modular approach, with separate components for each step. Specifically, we
focus on the second subtask, of generating coherent text given pre-selected
content in a multi-document setting. Concretely, we formalize Fusion-in-Context
(FiC) as a standalone task, whose input consists of source texts with
highlighted spans of targeted content. A model then needs to generate a
coherent passage that includes all and only the target information. Our work
includes the development of a curated dataset of 1000 instances in the reviews
domain, alongside a novel evaluation framework for assessing the faithfulness
and coverage of highlights, which strongly correlate to human judgment. Several
baseline models exhibit promising outcomes and provide insightful analyses.
This study lays the groundwork for further exploration of modular text
generation in the multi-document setting, offering potential improvements in
the quality and reliability of generated content. Our benchmark, FuseReviews,
including the dataset, evaluation framework, and designated leaderboard, can be
found at https://fusereviews.github.io/.
