@inproceedings{yang-etal-2023-oasum,
    title = "{OAS}um: Large-Scale Open Domain Aspect-based Summarization",
    author = "Yang, Xianjun  and
      Song, Kaiqiang  and
      Cho, Sangwoo  and
      Wang, Xiaoyang  and
      Pan, Xiaoman  and
      Petzold, Linda  and
      Yu, Dong",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.268",
    doi = "10.18653/v1/2023.findings-acl.268",
    pages = "4381--4401",
    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, on a relatively small scale, or contains 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 and automatically create a high-quality, large-scale open-domain aspect-based 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 fine-tuning 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 pre-trained checkpoints are publicly available.",
}

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, on a relatively small scale, or contains 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 and automatically create a high-quality, large-scale open-domain aspect-based 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 fine-tuning 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 pre-trained checkpoints are publicly available.
Yang, Xianjun Song, Kaiqiang Cho, Sangwoo Wang, Xiaoyang Pan, Xiaoman Petzold, Linda Yu, Dong
OASum: Large-Scale Open Domain Aspect-based Summarization