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This paper presents the results of the shared task on Lay Summarisation of Biomedical Research Articles (BioLaySumm), hosted at the BioNLP Workshop at ACL 2023.
Longformer: The long-document transformer
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SimCLS: A simple framework for contrastive learning of abstractive summarization
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Factorizing content and budget decisions in abstractive summarization of long documents
Marcio Fonseca, Yftah Ziser, and Shay B. Cohen. 2022 · 2022
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Making science simple: Corpora for the lay summarisation of scientific literature
Tomas Goldsack, Zhihao Zhang, Chenghua Lin, and Carolina Scarton. 2022 · 2022
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How far are we from robust long abstractive summarization?
Huan Yee Koh, Jiaxin Ju, He Zhang, Ming Liu, and Shirui Pan. 2022 · 2022
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Readability controllable biomedical document summarization
Zheheng Luo, Qianqian Xie, and Sophia Ananiadou. 2022b · 2022
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Ncuee-nlp at biolaysumm task 2: Readability-controlled summarization of biomedical articles using the primera models
Chao-Yi Chen, Jen-Hao Yang, and Lung-Hao Lee. 2023 · 2023
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Lhs712ee at biolaysumm 2023: Using bart and led to summarize biomedical research articles
Quancheng Liu, Xiheng Ren, and V. G. Vinod Vydiswaran. 2023 · 2023
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Vbd-nlp at biolaysumm task 1: Explicit and implicit key information selection for lay summarization on biomedical long documents
Phuc Phan, Tri Huu Tran, and Hai-Long Trieu. 2023 · 2023
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Aptsumm at biolaysumm task 1: Biomedical breakdown, improving readability by relevancy based selection
A.S. Poornash, Atharva Deshmukh, Archit Sharma, and Sriparna Saha. 2023 · 2023
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Iitr at biolaysumm task 1:lay summarization of biomedical articles using transformers
Venkat praneeth Reddy, Pinnapu Reddy, Harshavardhan Reddy, Karanam Sai Sumedh, and Raksha Sharma. 2023 · 2023
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PRIMERA: Pyramid-based masked sentence pre-training for multi-document summarization
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BioBART: Pretraining and evaluation of a biomedical generative language model
Hongyi Yuan, Zheng Yuan, Ruyi Gan, Jiaxing Zhang, Yutao Xie, and Sheng Yu. 2022 · 2022
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Pathology dynamics at biolaysumm: the trade-off between readability, relevance, and factuality in lay summarization
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BioGPT: generative pre-trained transformer for biomedical text generation and mining
Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon, and Tie-Yan Liu. 2022a
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Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. 2020a
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Grasum at biolaysumm task 1: Background knowledge grounding for readable, relevant, and factual biomedical lay summaries
Domenic Rosati. 2023 · 2023
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Csiro data61 team at biolaysumm task 1: Lay summarisation of biomedical research articles using generative models
Mong Yuan Sim, Maciej Rybinski, and Sarvnaz Karimi. 2023 · 2023
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Mdc at biolaysumm task 1: Evaluating gpt models for biomedical lay summarization
Oisín Turbitt and Robert Bevan. 2023 · 2023
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Ikm_lab at biolaysumm task 1: Longformer-based prompt tuning for biomedical lay summary generation
Yu-Hsuan Wu, Ying-Jia Lin, and Hung-Yu Kao. 2023 · 2023
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