Rapid growth of the biomedical literature has led to many advances in the biomedical text mining field. Among the vast amount of information, biomedical article abstracts are the easily accessible sources. However, the number of the structured abstracts, describing the rhetorical sections with one of Background, Objective, Method, Result and Conclusion categories is still not considerable. Exploration of valuable information in the biomedical abstracts can be expedited with the improvements in the sequential sentence classification task. Deep learning based models has great performance/potential in achieving significant results in this task. However, they can often be overly complex and overfit to specific data. In this project, we study a state-of-the-art deep learning model, which we called SSN-4 model here. We investigate different components of the SSN-4 model to study the trade-off between the performance and complexity. We explore how well this model generalizes to a new data set beyond Randomized Controlled Trials (RCT) dataset. We address the question that whether word embeddings can be adjusted to the task to improve the performance. Furthermore, we develop a second model that addresses the confusion pairs in the first model. Results show that SSN-4 model does not appear to generalize well beyond RCT dataset.
NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities
Lomonosov Moscow State University（莫斯科罗蒙诺索夫国立大学）；Madan Bhandari University of Science and Technology（马丹·班达里科技大学）；Ural Federal University（乌拉尔联邦大学）；HSE University（高等经济大学）；Innopolis University（因诺波利斯大学）；A.P. Ershov Institute of Informatics Systems（A.P. 埃尔绍夫信息学系统研究所）；Artificial Intelligence Research Institute（人工智能研究所）；Sber AI
MedConclusion: A Benchmark for Biomedical Conclusion Generation from Structured Abstracts
Harvard AI and Robotics Lab, Harvard Medical School（哈佛大学医学院哈佛人工智能与机器人实验室）；University of Southern California（南加州大学）；Carnegie Mellon University（卡内基梅隆大学）；Stanford University（斯坦福大学）；Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University（哈佛大学肯普纳自然与人工智能研究所）
A Dataset for Plain Language Adaptation of Biomedical Abstracts
Lister Hill National Center for Biomedical Communications（李斯特·希尔国家生物医学交流中心）；U.S. National Library of Medicine（美国国家医学图书馆）；National Institutes of Health（美国国立卫生研究院）
尽管与健康相关的文献呈指数级增长，但科学文章的语言可能难以让公众理解。因此，将这种专家级语言改编成通俗语言版本对于公众可靠地理解大量与健康相关的文献是必要的。用于自动适应的深度学习算法是一种可能的解决方案;然而，需要黄金标准数据集进行适当的评估。到目前为止，建议的数据集包括成对的可比较的专业和一般公众面对的文件或从这些文件中挖掘出的语义相似的句子。这导致了不完美的比对和小测试集之间的权衡。为了解决这个问题，我们创建了Plain Language Adaptation of Biomedical Abstracts数据集。此数据集是第一个手动调整的数据集，它既与文档对齐，又与文档对齐。该数据集包含750个改编摘要，共计7643个句子对。在描述数据集的同时，我们还使用最先进的深度学习方法对数据集进行了自动适应基准测试，为未来的研究设定了基线。
NeuroSym-BioCAT: Leveraging Neuro-Symbolic Methods for Biomedical Scholarly Document Categorization and Question Answering
TIB—Leibniz Information Centre for Science and Technology（莱布尼茨科学与技术信息中心（TIB））；L3S Research Center, Leibniz University Hannover（汉诺威莱布尼茨大学L3S研究中心）；Daffodil International University（水仙国际大学）
Lessons from the TREC Plain Language Adaptation of Biomedical Abstracts (PLABA) track
Yale School of Medicine（耶鲁医学院）；Tufts University（塔夫茨大学）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；Johns Hopkins University（约翰霍普金斯大学）；Georgia Institute of Technology（佐治亚理工学院）；National Institute of Standards（国家标准研究院）；National Library of Medicine（国家医学图书馆）
MedDistant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction
German Research Center for Artificial Intelligence（德国人工智能研究中心）；Saarland University（萨尔大学）；Yale Center for Medical Informatics（耶鲁医学信息学中心）；UCL Centre for Artificial Intelligence（伦敦大学学院人工智能中心）
BioDivergence: A Benchmark and Evaluation Framework for Hidden Contextual Contradictions in Biomedical Abstracts
College of Engineering and Computer Science, University of Central Florida（中佛罗里达大学工程与计算机科学学院）；Burnett School of Biomedical Sciences, University of Central Florida（中佛罗里达大学伯内特生物医学科学学院）
Benchmarking Large Language Models for Biomedical Relation Extraction
Interdisciplinary School of Doctoral Studies（跨学科博士研究学院）；Faculty of Mathematics and Computer Science, University of Bucharest（布加勒斯特大学数学与计算机科学学院）；HLT Research Center（HLT研究中心）
Delving into LLM-assisted writing in biomedical publications through excess vocabulary
Hertie Institute for AI in Brain Health, University of Tübingen, Germany（人工智能与脑健康赫尔蒂研究所、图宾根大学，德国）；Northwestern University, Evanston, Illinois, USA（西北大学，埃文斯顿，伊利诺伊州，美国）
Large-scale investigation of weakly-supervised deep learning for the fine-grained semantic indexing of biomedical literature
Institute of Informatics and Telecommunications, NCSR Demokritos（希腊国家科学研究中心德谟克利特信息学与电信研究所）；School of Informatics, Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学信息学院）；Department of Computer Engineering and Informatics, University of Patras（帕特雷大学计算机工程与信息学系）
Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆国家生物技术信息中心）
IHC-LLMiner: Automated extraction of tumour immunohistochemical profiles from PubMed abstracts using large language models
Institute of Health Informatics, University College London（伦敦大学学院健康信息学研究所）；Research Department of Pathology, University College London（伦敦大学学院病理学研究部）；Department of Pathology, Hadassah-Hebrew University Medical Center（哈达萨-希伯来大学医学中心病理学系）；Department of Cellular Pathology, University College London Hospitals NHS Foundation Trust（伦敦大学学院医院NHS信托基金会细胞病理学系）；School of Health & Wellbeing, University of Glasgow（格拉斯哥大学健康与福祉学院）
Ensembles of Large Language Models for Identifying EQ-5D Studies in PubMed Based on Their Abstracts
Doctoral School of Applied Informatics and Applied Mathematics, Obuda University（欧布达大学应用信息学与应用数学博士学院）；John von Neumann Faculty of Informatics, Obuda University（欧布达大学约翰·冯·诺伊曼信息学学院）；Doctoral School of Innovation Management, Obuda University（欧布达大学创新管理博士学院）
Rapid Biomedical Research Classification: The Pandemic PACT Advanced Categorisation Engine
NLPie Research（NLPie研究所）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Sharif University of Technology（谢里夫理工大学）；Pandemic Sciences Institute, University of Oxford（牛津大学流行病科学研究所）；Green Templeton College, University of Oxford（牛津大学格林坦普尔顿学院）；UK Collaborative on Development Research（英国发展研究合作组织）；Oxford-Suzhou Centre for Advanced Research（牛津苏州高等研究院）
本文提出 Pandemic PACT 高级分类引擎（PPACE），一种基于大型语言模型生成理由并微调小模型的自动分类方法，用于按 WHO 优先事项对生物医学研究摘要进行多标签分类，显著优于基线，助力全球卫生决策。
