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。

多重评论融合上下文

Multi-Review Fusion-in-Context

Grounded text generation systems often generate text that contains factual
inconsistencies, hindering their real-world applicability. Automatic factual
consistency evaluation may help alleviate this limitation by accelerating
evaluation cycles, filtering inconsistent outputs and augmenting training data.
While attracting increasing attention, such evaluation metrics are usually
developed and evaluated in silo for a single task or dataset, slowing their
adoption. Moreover, previous meta-evaluation protocols focused on system-level
correlations with human annotations, which leave the example-level accuracy of
such metrics unclear. In this work, we introduce TRUE: a comprehensive survey
and assessment of factual consistency metrics on a standardized collection of
existing texts from diverse tasks, manually annotated for factual consistency.
Our standardization enables an example-level meta-evaluation protocol that is
more actionable and interpretable than previously reported correlations,
yielding clearer quality measures. Across diverse state-of-the-art metrics and
11 datasets we find that large-scale NLI and question
generation-and-answering-based approaches achieve strong and complementary
results. We recommend those methods as a starting point for model and metric
developers, and hope TRUE will foster progress towards even better evaluation
methods.

TRUE 评测了多个评估度量的实际应用，并推荐大规模 NLI 及基于生成问答的方法作为模型和度量开发者的起点，以期推动更好的评估方法的进步。