ESBM

ESBM是由南京大学国家软件新技术重点实验室创建的数据集，旨在解决现有实体摘要评估基准的不足。该数据集包含175个从两个不同数据集中抽样的异构实体，由30位专家创建了2,100个通用目的的地面实况摘要。ESBM支持多种应用，如搜索引擎中的实体卡片展示、文档背景知识丰富及人类参与的研究活动。数据集的创建过程涉及从DBpedia和LinkedMDB中选择实体，并由专家独立创建摘要。ESBM的应用领域广泛，主要用于解决信息过载问题，帮助用户快速识别所需信息。

ESBM is a dataset developed by the State Key Laboratory for Novel Software Technology at Nanjing University, aiming to address the limitations of existing entity summarization evaluation benchmarks. This dataset includes 175 heterogeneous entities sampled from two separate datasets, with 2,100 general-purpose ground-truth summaries created by 30 experts. ESBM supports a variety of applications, such as entity card display in search engines, enrichment of background knowledge in documents, and human-involved research activities. The dataset construction process involves selecting entities from DBpedia and LinkedMDB, with summaries independently generated by experts. ESBM has a wide range of application scenarios, mainly used to solve the problem of information overload and help users quickly identify the required information.