EMNLP Findings2022
M3: Multi-level dataset for Multi-document summarisation of Medical studies
Yulia Otmakhova, Karin Verspoor, Timothy Baldwin, Antonio Jimeno-Yepes, Jey Han Lau
Abstract
We present M3 (Multi-level dataset for Multidocument summarisation of Medical studies), a benchmark dataset for evaluating the quality of summarisation systems in the biomedical domain. The dataset contains sets of multiple input documents and target summaries of three levels of complexity: documents, sentences, and propositions. The dataset also includes several levels of annotation, including biomedical entities, direction, and strength of relations between them, and the discourse relationships between the input documents ("contradiction" or "agreement"). We showcase usage scenarios of the dataset by testing 10 generic and domainspecific summarisation models in a zero-shot setting, and introduce a probing task based on counterfactuals to test if models are aware of the direction and strength of the conclusions generated from input studies.
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Builds on7
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- PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive SummarizationJingqing Zhang, Yao Zhao, Mohammad Saleh, Peter J. LiuICML 2020 · 2,453 citations
- BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and ComprehensionMike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad et al.ACL 2020 · 1,224 citations
- PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document SummarizationWen Xiao, Iz Beltagy, Giuseppe Carenini, Arman CohanACL 2022 · 147 citations
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