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,
resulting 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 and state-of-the-art
large language models (LLMs). Additionally, we explore the effectiveness of
extractive versus abstractive summarization within the scholarly domain on the
basis of automatically discovered aspects. Our results corroborate previous
findings in the general domain and indicate the general superiority of
end-to-end aspect-based summarization. Our data is released at
this https URL

提供了一个由领域专家精心制作和评估的新型摘要数据集 ACLSum，集成了多个方面的科学论文摘要，通过对预训练语言模型和最先进的大型语言模型（LLMs）的性能进行广泛实验，探索学术领域中抽取式与生成式摘要的有效性，结果证实了在学术领域中端到端基于方面的摘要的普遍优越性。