OASum: Large-Scale Open Domain Aspect-based Summarization

TL;DR

This paper introduces OASum, a large-scale open-domain aspect-based summarization dataset with over 3.7 million instances, enabling improved aspect-focused summarization and downstream task performance.

Contribution

The creation of a high-quality, large-scale open-domain dataset for aspect-based summarization and demonstration of its effectiveness through benchmark and transfer learning experiments.

Findings

Pre-trained models on OASum outperform backbone models in aspect-focused generation.

Zero-shot and few-shot learning on downstream datasets show strong performance.

OASum dataset and checkpoints are publicly available for research.

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.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.