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    <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 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.</abstract>
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%0 Conference Proceedings
%T OpenAsp: A Benchmark for Multi-document Open Aspect-based Summarization
%A Amar, Shmuel
%A Schiff, Liat
%A Ernst, Ori
%A Shefer, Asi
%A Shapira, Ori
%A Dagan, Ido
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F amar-etal-2023-openasp
%X 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 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.
%R 10.18653/v1/2023.emnlp-main.121
%U https://aclanthology.org/2023.emnlp-main.121
%U https://doi.org/10.18653/v1/2023.emnlp-main.121
%P 1967-1991

Markdown (Informal)

[OpenAsp: A Benchmark for Multi-document Open Aspect-based Summarization](https://aclanthology.org/2023.emnlp-main.121) (Amar et al., EMNLP 2023)

• OpenAsp: A Benchmark for Multi-document Open Aspect-based Summarization (Amar et al., EMNLP 2023)

ACL

• Shmuel Amar, Liat Schiff, Ori Ernst, Asi Shefer, Ori Shapira, and Ido Dagan. 2023. OpenAsp: A Benchmark for Multi-document Open Aspect-based Summarization. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 1967–1991, Singapore. Association for Computational Linguistics.