SUMIE: A Synthetic Benchmark for Incremental Entity Summarization

Eunjeong Hwang, Yichao Zhou, Beliz Gunel, James Bradley Wendt, Sandeep Tata

Abstract: 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 , a fully synthetic dataset designed to expose real-world IES challenges. This dataset addresses issues like incorrect entity association and incomplete information, capturing real-world complexity by generating diverse attributes, summaries, and unstructured paragraphs with 99% alignment accuracy between generated summaries and paragraphs. 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.