ACLSum: A New Dataset for Aspect-based Summarization of Scientific Publications

Sotaro Takeshita, Tommaso Green, Ines Reinig, Kai Eckert, Simone Ponzetto

Abstract
Extensive efforts in the past have been directed toward the development of summarization datasets. However, a predominant number of these resources have been (semi)-automatically generated, typically through web data crawling. This resulted in subpar resources for training and evaluating summarization systems, a quality compromise that is arguably due to the substantial costs associated with generating ground-truth summaries, particularly for diverse languages and specialized domains. To address this issue, we present ACLSum, a novel summarization dataset carefully crafted and evaluated by domain experts. In contrast to previous datasets, ACLSum facilitates multi-aspect summarization of scientific papers, covering challenges, approaches, and outcomes in depth. Through extensive experiments, we evaluate the quality of our resource and the performance of models based on pretrained language models (PLMs) and state-of-the-art large language models (LLMs). Additionally, we explore the effectiveness of extract-then-abstract versus abstractive end-to-end summarization within the scholarly domain on the basis of automatically discovered aspects. While the former performs comparably well to the end-to-end approach with pretrained language models regardless of the potential error propagation issue, the prompting-based approach with LLMs shows a limitation in extracting sentences from source documents.
Anthology ID:
2024.naacl-long.371
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6660–6675
Language:
URL:
https://aclanthology.org/2024.naacl-long.371
DOI:
10.18653/v1/2024.naacl-long.371
Bibkey:
Cite (ACL):
Sotaro Takeshita, Tommaso Green, Ines Reinig, Kai Eckert, and Simone Ponzetto. 2024. ACLSum: A New Dataset for Aspect-based Summarization of Scientific Publications. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 6660–6675, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
ACLSum: A New Dataset for Aspect-based Summarization of Scientific Publications (Takeshita et al., NAACL 2024)
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https://preview.aclanthology.org/landing_page/2024.naacl-long.371.pdf
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