Year: 2023
Type: conference-paper
Abstract: Aspect or query-based summarization has recently caught more attention, as it can generate differentiated summaries based on users' interests.However, the current dataset for aspect or query-based summarization either focuses on specific domains, contains relatively small-scale instances, or includes only a few aspect types.Such limitations hinder further explorations in this direction.In this work, we take advantage of crowd-sourcing knowledge on Wikipedia.organd automatically create a high-quality, large-scale open-domain aspectbased summarization dataset named OASum, which contains more than 3.7 million instances with around 1 million different aspects on 2 million Wikipedia pages.We provide benchmark results on OASum and demonstrate its ability for diverse aspect-based summarization generation.To overcome the data scarcity problem on specific domains, we also perform zero-shot, few-shot, and fine-tuning on seven downstream datasets.Specifically, zero/few-shot and finetuning results show that the model pre-trained on our corpus demonstrates a strong aspect or query-focused generation ability compared with the backbone model.Our dataset and pretrained checkpoints are publicly available.1
Institutions: University of California, Santa Barbara, Tencent (China)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4385570192