WikiAsp: A Dataset for Multi-domain Aspect-based Summarization
This provides a resource for researchers working on open-domain aspect-based summarization, though it is incremental as it builds on existing summarization tasks with a new dataset.
The authors tackled the lack of a multi-domain dataset for aspect-based summarization by creating WikiAsp, a large-scale dataset from Wikipedia articles across 20 domains, and found that existing models struggle with challenges like pronoun handling and time-sensitive explanations.
Aspect-based summarization is the task of generating focused summaries based on specific points of interest. Such summaries aid efficient analysis of text, such as quickly understanding reviews or opinions from different angles. However, due to large differences in the type of aspects for different domains (e.g., sentiment, product features), the development of previous models has tended to be domain-specific. In this paper, we propose WikiAsp, a large-scale dataset for multi-domain aspect-based summarization that attempts to spur research in the direction of open-domain aspect-based summarization. Specifically, we build the dataset using Wikipedia articles from 20 different domains, using the section titles and boundaries of each article as a proxy for aspect annotation. We propose several straightforward baseline models for this task and conduct experiments on the dataset. Results highlight key challenges that existing summarization models face in this setting, such as proper pronoun handling of quoted sources and consistent explanation of time-sensitive events.