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
\textit{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. \footnote{Our
benchmark, FuseReviews, including the dataset, evaluation framework and
designated leaderboard, can be found at https://fusereviews.github.io/.}

本研究提出了一种模块化的方法，用于在多文档环境中生成包含目标信息的连贯文本，为进一步探索模块化文本生成提供了基础，提高了生成内容的质量和可靠性。