OpenAsp: A Benchmark for Multi-document Open Aspect-based Summarization

• URL: http://arxiv.org/abs/2312.04440v1
• Date: Thu, 7 Dec 2023 17:06:20 GMT
• Title: OpenAsp: A Benchmark for Multi-document Open Aspect-based Summarization
• Authors: Shmuel Amar, Liat Schiff, Ori Ernst, Asi Shefer, Ori Shapira and Ido Dagan
• Abstract summary: We introduce OpenAsp, a benchmark for aspect-based summarization. We show that the realistic open-aspect setting realized in OpenAsp poses a challenge for current state-of-the-art summarization models.
• Score: 19.079053035229695
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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