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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

摘要

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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引用它的顶会 Paper1

  1. MedEval: A Multi-Level, Multi-Task, and Multi-Domain Medical Benchmark for Language Model EvaluationZexue He, Yu Wang, An Yan, Yao Liu 等EMNLP 2023 · 7 次引用

它引用的 Paper7

  1. BERTScore: Evaluating Text Generation with BERTTianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger 等ICLR 2020 · 8,443 次引用
  2. Big Bird: Transformers for Longer SequencesManzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie 等NeurIPS 2020 · 3,159 次引用
  3. PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive SummarizationJingqing Zhang, Yao Zhao, Mohammad Saleh, Peter J. LiuICML 2020 · 2,453 次引用
  4. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and ComprehensionMike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad 等ACL 2020 · 1,224 次引用
  5. PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document SummarizationWen Xiao, Iz Beltagy, Giuseppe Carenini, Arman CohanACL 2022 · 147 次引用

相关 Paper

  1. MS2: Multi-Document Summarization of Medical StudiesJay DeYoung, Iz Beltagy, Madeleine van Zuylen, Bailey Kuehl 等EMNLP 2021 · 83 次引用
  2. Automated Metrics for Medical Multi-Document Summarization Disagree with Human EvaluationsLucy Lu Wang, Yulia Otmakhova, Jay DeYoung, Thinh Hung Truong 等ACL 2023 · 12 次引用
  3. Understanding Faithfulness and Reasoning of Large Language Models on Plain Biomedical SummariesBiaoyan Fang, Xiang Dai, Sarvnaz KarimiEMNLP Findings 2024 · 4 次引用
  4. The Power of Summary-Source AlignmentsOri Ernst, Ori Shapira, Aviv Slobodkin, Sharon Adar 等ACL Findings 2024 · 2 次引用
  5. The patient is more dead than alive: exploring the current state of the multi-document summarisation of the biomedical literatureYulia Otmakhova, Karin Verspoor, Timothy Baldwin, Jey Han LauACL 2022
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