LED down the rabbit hole: exploring the potential of global attention for biomedical multi-document summarisation
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Abstract (arXiv)
In this paper we report on our submission to the Multidocument Summarisation for Literature Review (MSLR) shared task. Specifically, we adapt PRIMERA (Xiao et al., 2022) to the biomedical domain by placing global attention on important biomedical entities in several ways. We analyse the outputs of the 23 resulting models, and report patterns in the results related to the presence of additional global attention, number of training steps, and the input configuration.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 7 of its 10 references that are in the map. First posted on arXiv 19 Sep 2022.
Builds on
7 of 10 references.
References · 7
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- 2020Longformer: The Long-Document Transformercited by 3,362
- 2022LinkBERT: Pretraining Language Models with Document Linkscited by 209
- 2021MS2: Multi-Document Summarization of Medical Studiescited by 80
- 2018A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literaturecited by 79
- 2021PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarizationcited by 62
- 2020Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarizationcited by 52
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Citing papers per year: 2023: 2.
2 papers.