The performance of automatic summarization models has improved dramatically in recent years.Yet, there is still a gap in meeting specific information needs of users in real-world scenarios, particularly when a targeted summary is sought, such as in the useful aspectbased summarization setting targeted in this paper.Previous datasets and studies for this setting have predominantly concentrated on a limited set of pre-defined aspects, focused solely on single document inputs, or relied on synthetic data.To advance research on more realistic scenarios, we introduce OPENASP, a benchmark for multi-document open aspect-based summarization.This benchmark is created using a novel and cost-effective annotation protocol, by which an open aspect dataset is derived from existing generic multi-document summarization datasets.We analyze the properties of OPENASP showcasing its high-quality content.Further, we show that the realistic open-aspect setting realized in OPENASP poses a challenge for current state-of-the-art summarization models, as well as for large language models.