  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 aspect-based 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
\textit{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.
