SQuALITY: Building a Long-Document Summarization Dataset the Hard Way

Alex Wang, Richard Yuanzhe Pang, Angelica Chen, Jason Phang, Samuel R. Bowman

Abstract
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.
Anthology ID:
2022.emnlp-main.75
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
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Publisher:
Association for Computational Linguistics
Note:
Pages:
1139–1156
Language:
URL:
https://aclanthology.org/2022.emnlp-main.75
DOI:
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Cite (ACL):
Alex Wang, Richard Yuanzhe Pang, Angelica Chen, Jason Phang, and Samuel R. Bowman. 2022. SQuALITY: Building a Long-Document Summarization Dataset the Hard Way. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1139–1156, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
SQuALITY: Building a Long-Document Summarization Dataset the Hard Way (Wang et al., EMNLP 2022)
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https://preview.aclanthology.org/ingestion-script-update/2022.emnlp-main.75.pdf
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