No existing dataset adequately tests how well language models can
incrementally update entity summaries - a crucial ability as these models
rapidly advance. The Incremental Entity Summarization (IES) task is vital for
maintaining accurate, up-to-date knowledge. To address this, we introduce
SUMIE, a fully synthetic dataset designed to expose real-world IES challenges.
This dataset effectively highlights problems like incorrect entity association
and incomplete information presentation. Unlike common synthetic datasets, ours
captures the complexity and nuances found in real-world data. We generate
informative and diverse attributes, summaries, and unstructured paragraphs in
sequence, ensuring high quality. The alignment between generated summaries and
paragraphs exceeds 96%, confirming the dataset's quality. Extensive experiments
demonstrate the dataset's difficulty - state-of-the-art LLMs struggle to update
summaries with an F1 higher than 80.4%. We will open source the benchmark and
the evaluation metrics to help the community make progress on IES tasks.

SUMIE 是一个完全合成的数据集，用于暴露现实世界的增量实体摘要挑战，验证了生成的摘要与段落之间的高质量一致性，该数据集难度高，先进的语言模型在更新摘要时的 F1 得分超过 80.4%。