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Clustering of Deep Contextualized Representations for Summarization of Biomedical Texts [article]

Milad Moradi, Matthias Samwald
2019 arXiv   pre-print
In recent years, summarizers that incorporate domain knowledge into the process of text summarization have outperformed generic methods, especially for summarization of biomedical texts.  ...  In this paper, we demonstrate that contextualized representations extracted from the pre-trained deep language model BERT, can be effectively used to measure the similarity between sentences and to quantify  ...  ,  Showing that clustering of deep contextualized representations can improve the performance of biomedical text summarization.  ... 
arXiv:1908.02286v2 fatcat:3pa62vuvi5hnrg5fkvvm7fsjmq

Discovering Thematically Coherent Biomedical Documents Using Contextualized Bidirectional Encoder Representations from Transformers-Based Clustering

Khishigsuren Davagdorj, Ling Wang, Meijing Li, Van-Huy Pham, Keun Ho Ryu, Nippon Theera-Umpon
2022 International Journal of Environmental Research and Public Health  
Moreover, empirical investigations demonstrated that traditional text clustering methods produce unsatisfactory results in terms of non-contextualized vector space representations because that neglect  ...  Text Mining) BioBERT domain-specific language representations to enhance the clustering accuracy.  ...  Acknowledgments: The authors would like to thank reviewers for their essential suggestions to improve the manuscript. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ijerph19105893 pmid:35627429 pmcid:PMC9141535 fatcat:z6tzrvefizburae6tpkbnragke

Automated Multi-Document Biomedical Text Summarization Using Deep Learning Model

Ahmed S. Almasoud, Siwar Ben Haj Hassine, Fahd N. Al-Wesabi, Mohamed K. Nour, Anwer Mustafa Hilal, Mesfer Al Duhayyim, Manar Ahmed Hamza, Abdelwahed Motwakel
2022 Computers Materials & Continua  
This paper presents a Deep Learning based Attention Long Short Term Memory (DL-ALSTM) Model for Multi-document Biomedical Text Summarization.  ...  To handle the huge amount of biomedical data, automated multi-document biomedical text summarization becomes an effective and robust approach of accessing the increased amount of technical and medical  ...  Acknowledgement: The authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges (APC) of this publication.  ... 
doi:10.32604/cmc.2022.024556 fatcat:sj5x7gn6avgyviq6zgo4rdtowu

An overview of event extraction and its applications [article]

Jiangwei Liu, Liangyu Min, Xiaohong Huang
2021 arXiv   pre-print
With the rapid development of information technology, online platforms have produced enormous text resources.  ...  Finally, we summarize the common issues, current solutions, and future research directions.  ...  [67] propose a deep model, integrating GCN and Transformer, to generate structured contextual representations based on the dependency parse results.  ... 
arXiv:2111.03212v1 fatcat:o3oagnjrybh3vapvvp7twgjtuu

Deep Learning, Natural Language Processing, and Explainable Artificial Intelligence in the Biomedical Domain [article]

Milad Moradi, Matthias Samwald
2022 arXiv   pre-print
Deep learning methods are then described in Section 2.  ...  In Section 4, we give an introduction to explainable artificial intelligence and discuss the importance of explainability of artificial intelligence systems, especially in the biomedical domain.  ...  In order to address this issue, biomedical text summarizers employed sources of domain knowledge to map input text to a concept-based representation (Ji et al., 2017; Mishra et al., 2014; Moradi and Ghadiri  ... 
arXiv:2202.12678v2 fatcat:4nv42mbpuveb7euxkr4b6ojuxi

Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review [article]

Irene Li, Jessica Pan, Jeremy Goldwasser, Neha Verma, Wai Pan Wong, Muhammed Yavuz Nuzumlalı, Benjamin Rosand, Yixin Li, Matthew Zhang, David Chang, R. Andrew Taylor, Harlan M. Krumholz (+1 others)
2021 arXiv   pre-print
Well over half of the information stored within EHRs is in the form of unstructured text (e.g. provider notes, operation reports) and remains largely untapped for secondary use.  ...  In this survey paper, we summarize current neural NLP methods for EHR applications.  ...  Specifically, we summarize a broad range of existing literature on deep learning methods with clinical text data.  ... 
arXiv:2107.02975v1 fatcat:nayhw7gadfdzrovycdkvzy75pi

A Survey on Deep Learning for Named Entity Recognition [article]

Jing Li, Aixin Sun, Jianglei Han, Chenliang Li
2020 arXiv   pre-print
NER always serves as the foundation for many natural language applications such as question answering, text summarization, and machine translation.  ...  Next, we survey the most representative methods for recent applied techniques of deep learning in new NER problem settings and applications.  ...  Why Deep Learning for NER? Deep learning is a field of machine learning that is composed of multiple processing layers to learn representations of data with multiple levels of abstraction [87] .  ... 
arXiv:1812.09449v3 fatcat:36tnstbyo5h4xizjpqn4cevgui

Multilingual Medical Question Answering and Information Retrieval for Rural Health Intelligence Access [article]

Vishal Vinod, Susmit Agrawal, Vipul Gaurav, Pallavi R, Savita Choudhary
2021 arXiv   pre-print
We obtain promising results for this pipeline and preliminary results for EHR (Electronic Health Record) analysis with text summarization for medical practitioners to peruse for their diagnosis.  ...  Many of these regions are gradually gaining access to internet infrastructure, although not with a strong enough connection to allow for sustained communication with a medical practitioner.  ...  The medical reports and clinical data are much more complicated to general medical terms and features included in the deep representations of the clinical medical text and embedding.  ... 
arXiv:2106.01251v1 fatcat:hmbqc26khvexrffcechbqlitcq

Named Entity Recognition and Relation Detection for Biomedical Information Extraction

Nadeesha Perera, Matthias Dehmer, Frank Emmert-Streib
2020 Frontiers in Cell and Developmental Biology  
This information can be integrated into networks to summarize large-scale details on a particular biomedical or clinical problem, which is then amenable for easy data management and further analysis.  ...  For this reason, natural language processing (NLP) and text mining methods are used for information extraction from such publications.  ...  Cluster-based word representation: In clustering-based word representation, the basic idea is that each cluster of words should contain words with contextually similar information.  ... 
doi:10.3389/fcell.2020.00673 pmid:32984300 pmcid:PMC7485218 fatcat:khclwjfykjfi3jktvrbuliwidm

Learning for Biomedical Information Extraction: Methodological Review of Recent Advances [article]

Feifan Liu, Jinying Chen, Abhyuday Jagannatha, Hong Yu
2016 arXiv   pre-print
In addition, we dive into open information extraction and deep learning, two emerging and influential techniques and envision next generation of BioIE.  ...  Unlike existing reviews covering a holistic view on BioIE, this review focuses on mainly recent advances in learning based approaches, by systematically summarizing them into different aspects of methodological  ...  [48] combined clustering-based word representation and distributional word representation into a structural SVM learning scheme, showing better performance than using either single type of representation  ... 
arXiv:1606.07993v1 fatcat:7d5om7zxxzhoviiriasrfwg3xi

Comparison of Neural Language Modeling Pipelines For Outcome Prediction From Unstructured Medical Text Notes

Cherubin Mugisha, Incheon Paik
2022 IEEE Access  
On the one hand, statistic, semantic, and contextualized word embedding-based models and on the other hand preprocessing approaches are the keys to a better representation of a document.  ...  We conducted a deep analysis on text preprocessing tasks producing three datasets: raw data with minor preprocessing, meticulous preprocessing, and extreme preprocessing filtering only medicalrelated terminologies  ...  The biomedical language representation model for biomedical text mining (BioBERT) [21] is a domain-specific language model that has been trained on medical text data.  ... 
doi:10.1109/access.2022.3148279 fatcat:wohvhxg7djfo3ouy7t75iulhoy

Pre-trained Language Models in Biomedical Domain: A Systematic Survey [article]

Benyou Wang, Qianqian Xie, Jiahuan Pei, Prayag Tiwari, Zhao Li, Jie fu
2021 arXiv   pre-print
In this paper, we summarize the recent progress of pre-trained language models in the biomedical domain and their applications in biomedical downstream tasks.  ...  health records, protein, and DNA sequences for various biomedical tasks.  ...  The 'contextualized word embedding' largely improves the quality of word representation in various tasks [45] .  ... 
arXiv:2110.05006v2 fatcat:aykwfhgi4jgmfovissgdvknny4

The Inefficiency of Language Models in Scholarly Retrieval: An Experimental Walk-through [article]

Shruti Singh, Mayank Singh
2022 arXiv   pre-print
Our experiments showcase the inability to retrieve relevant documents for a short-query text even under the most relaxed conditions.  ...  Retrieval performance turns out to be more influenced by the surface form rather than the semantics of the text.  ...  The training corpus consists of 18% papers from the computer science domain and 82% from the broad biomedical domain. Full texts of the papers are used for training.  ... 
arXiv:2203.15364v1 fatcat:43lgvxckwvbb5ci4qmt5x6hxdy

BERTMeSH: Deep Contextual Representation Learning for Large-scale High-performance MeSH Indexing with Full Text [article]

Ronghui You, Yuxuan Liu, Hiroshi Mamitsuka, Shanfeng Zhu
2020 bioRxiv   pre-print
BERTMeSH has two technologies: 1) the state-of-the-art pre-trained deep contextual representation, BERT (Bidirectional Encoder Representations from Transformers), which makes BERTMeSH capture deep semantics  ...  of full text. 2) a transfer learning strategy for using both full text in PubMed Central (PMC) and title and abstract (only and no full text) in MEDLINE, to take advantages of both.  ...  This highlights the power of deep contextual representation for improving the performance of MeSH indexing.  ... 
doi:10.1101/2020.07.04.187674 fatcat:yhdlmxozozab5ll64jl7swbire

Automatic Multi Document Summarization Approaches

Kumar
2012 Journal of Computer Science  
Problem statement: Text summarization can be of different nature ranging from indicative summary that identifies the topics of the document to informative summary which is meant to represent the concise  ...  We will direct our focus notably on four well known approaches to multi document summarization namely the feature based method, cluster based method, graph based method and knowledge based method.  ...  Moreover, until now, most text summarization models incorporate only bag of words as text representation and do not include much contextual information.  ... 
doi:10.3844/jcssp.2012.133.140 fatcat:6wry32zaufczbkm7f62nor5ipq
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