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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.org 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.

Airline passengers walk past a robot providing tips for getting through security faster during a pilot project as they head toward a security checkpoint Tuesday, July 11, 2017, at Seattle-Tacoma International Airport, in SeaTac, Wash. Through audio instructions in English and on-screen animated instructions in six different languages, the robot told passengers to remove items such as scarves, jackets and belts, to empty their pockets and to prepare for a body scan before going through screening. During the pilot program, running in conjunction with the American Association of Airport Executives Innovation Forum, airport officials will track the number of times passengers trigger the body scanner alarm during and after the robot test.