SQuALITY: Building a Long-Document Summarization Dataset the Hard Way
This addresses the need for reliable benchmark data in summarization research, though it is incremental as it builds on existing datasets like QuALITY.
The researchers tackled the problem of creating a high-quality long-document summarization dataset by hiring contractors to write original summaries from scratch, resulting in SQuALITY, a challenging dataset where state-of-the-art systems struggle and automatic metrics are weak indicators of quality.
Summarization datasets are often assembled either by scraping naturally occurring public-domain summaries -- which are nearly always in difficult-to-work-with technical domains -- or by using approximate heuristics to extract them from everyday text -- which frequently yields unfaithful summaries. In this work, we turn to a slower but more straightforward approach to developing summarization benchmark data: We hire highly-qualified contractors to read stories and write original summaries from scratch. To amortize reading time, we collect five summaries per document, with the first giving an overview and the subsequent four addressing specific questions. We use this protocol to collect SQuALITY, a dataset of question-focused summaries built on the same public-domain short stories as the multiple-choice dataset QuALITY (Pang et al., 2021). Experiments with state-of-the-art summarization systems show that our dataset is challenging and that existing automatic evaluation metrics are weak indicators of quality.