CORD-19 Dataset Research Articles

CovSumm: an unsupervised transformer-cum-graph-based hybrid document summarization model for CORD-19.

The number of research articles published on COVID-19 has dramatically increased since the outbreak of the pandemic in November 2019. This absurd rate of productivity in research articles leads to information overload. It has increasingly become urgent for researchers and medical associations to stay up to date on the latest COVID-19 studies. To address information overload in COVID-19 scientific literature, the study presents a novel hybrid model named CovSumm, an unsupervised graph-based hybrid approach for single-document summarization, that is evaluated on the CORD-19 dataset. We have tested the proposed methodology on the scientific papers in the database dated from January 1, 2021 to December 31, 2021, consisting of 840 documents in total. The proposed text summarization is a hybrid of two distinctive extractive approaches (1) GenCompareSum (transformer-based approach) and (2) TextRank (graph-based approach). The sum of scores generated by both methods is used to rank the sentences for generating the summary. On the CORD-19, the recall-oriented understudy for gisting evaluation (ROUGE) score metric is used to compare the performance of the CovSumm model with various state-of-the-art techniques. The proposed method achieved the highest scores of ROUGE-1: 40.14%, ROUGE-2: 13.25%, and ROUGE-L: 36.32%. The proposed hybrid approach shows improved performance on the CORD-19 dataset when compared to existing unsupervised text summarization methods.

Avoiding background knowledge: literature based discovery from important information

BackgroundAutomatic literature based discovery attempts to uncover new knowledge by connecting existing facts: information extracted from existing publications in the form of A rightarrow B and B rightarrow C relations can be simply connected to deduce A rightarrow C. However, using this approach, the quantity of proposed connections is often too vast to be useful. It can be reduced by using subjectrightarrow(predicate)rightarrowobject triples as the A rightarrow B relations, but too many proposed connections remain for manual verification.ResultsBased on the hypothesis that only a small number of subject–predicate–object triples extracted from a publication represent the paper’s novel contribution(s), we explore using BERT embeddings to identify these before literature based discovery is performed utilizing only these, important, triples. While the method exploits the availability of full texts of publications in the CORD-19 dataset—making use of the fact that a novel contribution is likely to be mentioned in both an abstract and the body of a paper—to build a training set, the resulting tool can be applied to papers with only abstracts available. Candidate hidden knowledge pairs generated from unfiltered triples and those built from important triples only are compared using a variety of timeslicing gold standards.ConclusionsThe quantity of proposed knowledge pairs is reduced by a factor of 10^3, and we show that when the gold standard is designed to avoid rewarding background knowledge, the precision obtained increases up to a factor of 10. We argue that the gold standard needs to be carefully considered, and release as yet undiscovered candidate knowledge pairs based on important triples alongside this work.

Extraction of knowledge graph of Covid-19 through mining of unstructured biomedical corpora

The number of biomedical articles published is increasing rapidly over the years. Currently there are about 30 million articles in PubMed and over 25 million mentions in Medline. Among these fundamentals, Biomedical Named Entity Recognition (BioNER) and Biomedical Relation Extraction (BioRE) are the most essential in analysing the literature. In the biomedical domain, Knowledge Graph is used to visualize the relationships between various entities such as proteins, chemicals and diseases. Scientific publications have increased dramatically as a result of the search for treatments and potential cures for the new Coronavirus, but efficiently analysing, integrating, and utilising related sources of information remains a difficulty. In order to effectively combat the disease during pandemics like COVID-19, literature must be used quickly and effectively. In this paper, we introduced a fully automated framework consists of BERT-BiLSTM, Knowledge graph, and Representation Learning model to extract the top diseases, chemicals, and proteins related to COVID-19 from the literature. The proposed framework uses Named Entity Recognition models for disease recognition, chemical recognition, and protein recognition. Then the system uses the Chemical - Disease Relation Extraction and Chemical - Protein Relation Extraction models. And the system extracts the entities and relations from the CORD-19 dataset using the models. The system then creates a Knowledge Graph for the extracted relations and entities. The system performs Representation Learning on this KG to get the embeddings of all entities and get the top related diseases, chemicals, and proteins with respect to COVID-19.

Building an intelligent system for answering specialized questions about COVID-19

The paper discusses the design and implementation process of an intelligent system for answering specialized questions about COVID-19. The system is based on deep learning and transfer learning techniques and uses the popular CORD-19 dataset as a source of scientific knowledge about the problem domain. The experiments performed with the pilot version of the system are presented and the obtained results are analyzed. Conclusions are formulated about the applicability and the opportunities for improvement of the proposed approach.

Do We Need a Specific Corpus and Multiple High-Performance GPUs for Training the BERT Model? An Experiment on COVID-19 Dataset

The COVID-19 pandemic has impacted daily lives around the globe. Since 2019, the amount of literature focusing on COVID-19 has risen exponentially. However, it is almost impossible for humans to read all of the studies and classify them. This article proposes a method of making an unsupervised model called a zero-shot classification model, based on the pre-trained BERT model. We used the CORD-19 dataset in conjunction with the LitCovid database to construct new vocabulary and prepare the test dataset. For NLI downstream task, we used three corpora: SNLI, MultiNLI, and MedNLI. We significantly reduced the training time by 98.2639% to build a task-specific machine learning model, using only one Nvidia Tesla V100. The final model can run faster and use fewer resources than its comparators. It has an accuracy of 27.84%, which is lower than the best-achieved accuracy by 6.73%, but it is comparable. Finally, we identified that the tokenizer and vocabulary more specific to COVID-19 could not outperform the generalized ones. Additionally, it was found that BART architecture affects the classification results.

UGDAS: Unsupervised graph-network based denoiser for abstractive summarization in biomedical domain.

Abstractive summarization models can generate summary auto-regressively, but the quality is often impacted by the noise in the text. Learning cross-sentence relations is a crucial step in this task and the graph-based network is more effective to capture the sentence relationship. Moreover, knowledge is very important to distinguish the noise of the text in special domain. A novel model structure called UGDAS is proposed in this paper, which combines a sentence-level denoiser based on an unsupervised graph-network and an auto-regressive generator. It utilizes domain knowledge and sentence position information to denoise the original text and further improve the quality of generated summaries. We use the recently-introduced dataset CORD-19 (COVID-19 Open Research Dataset) on text summarization task, which contains large-scale data on coronaviruses. The experimental results show that our model achieves the SOTA (state-of-the-art) result on CORD-19 dataset and outperforms the related baseline models on the PubMed Abstract dataset.

Expediting knowledge acquisition by a web framework for Knowledge Graph Exploration and Visualization (KGEV): case studies on COVID-19 and Human Phenotype Ontology

BackgroundKnowledges graphs (KGs) serve as a convenient framework for structuring knowledge. A number of computational methods have been developed to generate KGs from biomedical literature and use them for downstream tasks such as link prediction and question answering. However, there is a lack of computational tools or web frameworks to support the exploration and visualization of the KG themselves, which would facilitate interactive knowledge discovery and formulation of novel biological hypotheses.MethodWe developed a web framework for Knowledge Graph Exploration and Visualization (KGEV), to construct and visualize KGs in five stages: triple extraction, triple filtration, metadata preparation, knowledge integration, and graph database preparation. The application has convenient user interface tools, such as node and edge search and filtering, data source filtering, neighborhood retrieval, and shortest path calculation, that work by querying a backend graph database. Unlike other KGs, our framework allows fast retrieval of relevant texts supporting the relationships in the KG, thus allowing human reviewers to judge the reliability of the knowledge extracted.ResultsWe demonstrated a case study of using the KGEV framework to perform research on COVID-19. The COVID-19 pandemic resulted in an explosion of relevant literature, making it challenging to make full use of the vast and heterogenous sources of information. We generated a COVID-19 KG with heterogenous information, including literature information from the CORD-19 dataset, as well as other existing knowledge from eight data sources. We showed the utility of KGEV in three intuitive case studies to explore and query knowledge on COVID-19. A demo of this web application can be accessed at http://covid19nlp.wglab.org. Finally, we also demonstrated a turn-key adaption of the KGEV framework to study clinical phenotypic presentation of human diseases by Human Phenotype Ontology (HPO), illustrating the versatility of the framework.ConclusionIn an era of literature explosion, the KGEV framework can be applied to many emerging diseases to support structured navigation of the vast amount of newly published biomedical literature and other existing biological knowledge in various databases. It can be also used as a general-purpose tool to explore and query gene-phenotype-disease-drug relationships interactively.

Contextual Query Expansion for Conducting Technology-Assisted Biomedical Reviews

Given a biomedical research question, formalizing a query for retrieving relevant evidence from Biomedical data-sets is an important methodological step in conducting literature reviews and has been the topic of a number of works on technology-assisted reviews. In this paper, we provide a deep structure that learns to effectively generate terms for expanding Biomedical queries for searching over the data-sets referenced by PubMed, the major search system for Biomedical studies. We use and fine-tune BERT for modelling contextual relationship among terms in the textual content. We evaluate our work on the CORD-19 data-set and show its effectiveness compared with the baselines

Extracting Experiment Statistics, Conditions, and Topics from Scientific Papers with STEREO

We address the problem of extracting reports of statistics along with information about the experiment conditions and experiment topics from scientific publications. A common writing style for statistical results are the recommendations of the American Psychology Association (APA). In practice, writing styles vary as reports are not 100\% following APA-style or parameters are not reported despite being mandatory. In addition, the statistics are not reported in isolation but in context of experiment conditions investigated and the general experiment topic. We address these challenges by proposing a flexible pipeline STEREO based on wrapper induction and unsupervised aspect detection to extract experiment statistics, conditions, and topics. Thus, in contrast to existing rule-based tools like statcheck with a pre-defined set of rules, we learn rules via induction. Hierarchical wrapper induction is applied to learn rules to extract the reported statistics. Challenge here is to apply wrapper induction on an information extraction task without having formatting landmarks as they can be exploited in HTML pages. Result of step 1 is a set of extracted statistic reports together with sentences in which the reports were found. This is used as input to step 2 of STEREO, which has two parts. We extract experiment conditions using a grammar-based wrapper. Furthermore, we identify the experiment topic using an unsupervised attention-based aspect extraction approach adapted to our problem domain. We applied our pipeline to the over 100,000 documents in the CORD-19 dataset. It required only 0.25% of the CORD-19 corpus (about 500 documents) to learn statistics extraction rules that cover 95% of the sentences in CORD-19. The statistic extraction has 100% precision on APA-conform statistics, which is identical with statcheck. In addition, STEREO can extract non-APA writing styles with 95% precision, which statcheck does not support. Extracting non-APA conform statistics is important as they make more than 99% of all $113$k extracted statistics. We could extract in 46% the correct conditions from APA-conform reports (30% for non-APA). The best model for topic extraction achieves a precision of 75% on statistics reported in APA style $73% for non-APA conform). We conclude that STEREO is a good foundation for automatic statistic extraction and future developments for scientific paper analysis. Particularly the extraction of non-APA conform reports is important and allows applications such as giving feedback to authors about what is missing and could be changed. Finally, STEREO complements existing metadata extraction tools and can be integrated in a general scientific paper analysis pipeline.

Execution Time Prediction for Cypher Queries in the Neo4j Database Using a Learning Approach

With database management systems becoming complex, predicting the execution time of graph queries before they are executed is one of the challenges for query scheduling, workload management, resource allocation, and progress monitoring. Through the comparison of query performance prediction methods, existing research works have solved such problems in traditional SQL queries, but they cannot be directly applied in Cypher queries on the Neo4j database. Additionally, most query performance prediction methods focus on measuring the relationship between correlation coefficients and retrieval performance. Inspired by machine-learning methods and graph query optimization technologies, we used the RBF neural network as a prediction model to train and predict the execution time of Cypher queries. Meanwhile, the corresponding query pattern features, graph data features, and query plan features were fused together and then used to train our prediction models. Furthermore, we also deployed a monitor node and designed a Cypher query benchmark for the database clusters to obtain the query plan information and native data store. The experimental results of four benchmarks showed that the average mean relative error of the RBF model reached 16.5% in the Northwind dataset, 12% in the FIFA2021 dataset, and 16.25% in the CORD-19 dataset. This experiment proves the effectiveness of our proposed approach on three real-world datasets.

Analyzing transfer learning impact in biomedical cross-lingual named entity recognition and normalization

BackgroundThe volume of biomedical literature and clinical data is growing at an exponential rate. Therefore, efficient access to data described in unstructured biomedical texts is a crucial task for the biomedical industry and research. Named Entity Recognition (NER) is the first step for information and knowledge acquisition when we deal with unstructured texts. Recent NER approaches use contextualized word representations as input for a downstream classification task. However, distributed word vectors (embeddings) are very limited in Spanish and even more for the biomedical domain.MethodsIn this work, we develop several biomedical Spanish word representations, and we introduce two Deep Learning approaches for pharmaceutical, chemical, and other biomedical entities recognition in Spanish clinical case texts and biomedical texts, one based on a Bi-STM-CRF model and the other on a BERT-based architecture.ResultsSeveral Spanish biomedical embeddigns together with the two deep learning models were evaluated on the PharmaCoNER and CORD-19 datasets. The PharmaCoNER dataset is composed of a set of Spanish clinical cases annotated with drugs, chemical compounds and pharmacological substances; our extended Bi-LSTM-CRF model obtains an F-score of 85.24% on entity identification and classification and the BERT model obtains an F-score of 88.80% . For the entity normalization task, the extended Bi-LSTM-CRF model achieves an F-score of 72.85% and the BERT model achieves 79.97%. The CORD-19 dataset consists of scholarly articles written in English annotated with biomedical concepts such as disorder, species, chemical or drugs, gene and protein, enzyme and anatomy. Bi-LSTM-CRF model and BERT model obtain an F-measure of 78.23% and 78.86% on entity identification and classification, respectively on the CORD-19 dataset.ConclusionThese results prove that deep learning models with in-domain knowledge learned from large-scale datasets highly improve named entity recognition performance. Moreover, contextualized representations help to understand complexities and ambiguity inherent to biomedical texts. Embeddings based on word, concepts, senses, etc. other than those for English are required to improve NER tasks in other languages.

A REVIEW ON QUESTION AND ANSWER SYSTEM FOR COVID-19 LITERATURE ON PRE-TRAINED MODELS

The COVID-19 literature has accelerated at a rapid pace and the Artificial Intelligence community as well as researchers all over the globe has the responsibility to help the medical community. The CORD-19 dataset contains various articles about COVID-19, SARS-CoV-2, and related corona viruses. Due to massive size of literature and documents it is difficult to find relevant and accurate pieces of information. There are question answering system using pre-trained models and fine-tuning them using BERT Transformers. BERT is a language model that powerfully learns from tokens and sentence-level training. The variants of BERT like ALBERT, DistilBERT, RoBERTa, SciBERT alongwith BioSentVec can be effective in training the model as they help in improving accuracy and increase the training speed. This will also provide the information on using SPECTER-document level relatedness like CORD 19 embeddings for pre-training a Transformer language model. This article will help in building the question answering model to facilitate the research and save the lives of people in the fight against COVID 19.

Queries related to COVID-19: a more effective retrieval through finetuned ALBERT with BM25L question answering system

PurposeThe purpose of this paper is to build a better question answering (QA) system that can furnish more improved retrieval of answers related to COVID-19 queries from the COVID-19 open research data set (CORD-19). As CORD-19 has an up-to-date collection of coronavirus literature, text mining approaches can be successfully used to retrieve answers pertaining to all coronavirus-related questions. The existing a lite BERT for self-supervised learning of language representations (ALBERT) model is finetuned for retrieving all COVID relevant information to scientific questions posed by the medical community and to highlight the context related to the COVID-19 query.Design/methodology/approachThis study presents a finetuned ALBERT-based QA system in association with Best Match25 (Okapi BM25) ranking function and its variant BM25L for context retrieval and provided high scores in benchmark data sets such as SQuAD for answers related to COVID-19 questions. In this context, this paper has built a QA system, pre-trained on SQuAD and finetuned it on CORD-19 data to retrieve answers related to COVID-19 questions by extracting semantically relevant information related to the question.FindingsBM25L is found to be more effective in retrieval compared to Okapi BM25. Hence, finetuned ALBERT when extended to the CORD-19 data set provided accurate results.Originality/valueThe finetuned ALBERT QA system was developed and tested for the first time on the CORD-19 data set to extract context and highlight the span of the answer for more clarity to the user.

HITS-based attentional neural model for abstractive summarization

Automatic abstractive summary generation is still an open problem in natural language processing field. Conventional encoder–decoder model based abstractive summarization methods often suffer from repetition and semantic irrelevance. Recent studies apply traditional attention or graph-based attention on the encoder–decoder model to tackle the problem, under the assumption that all the sentences in the original document are indistinguishable from each other. But in a document, the same words in different sentences are not equally important, i.e., the words in a trivial sentence are less important than the words in a salient sentence. Based on it, we develop a HITS-based attention mechanism in this paper, which fully leverages sentence-level and word-level information by considering sentences and words in the original document as authorities and hubs. Based on it, we present a novel abstractive summarization method, with Kullback–Leibler (KL) divergence to refine the attention value, meanwhile we propose a comparison mechanism in summary generation to further improve the summarization performance. When evaluated on the CNN/Daily Mail and NYT datasets, the experimental results demonstrate the improvement of summarization performance and show the performance of our proposed method is comparable with that of the other summarization methods. Besides, we also conduct experiments on CORD-19 dataset (COVID-19 Open Research Dataset) which is a biomedical domain dataset, and the experimental results show great performance of our proposed method compared with that of the other state-of-the-art summarization methods.

Automatic Text Summarization of COVID-19 Research Articles Using Recurrent Neural Networks and Coreference Resolution

Purpose: Pandemic COVID-19 has created an emergency for the medical community. Researchers require extensive study of scientific literature in order to discover drugs and vaccines. In this situation where every minute is valuable to save the lives of hundreds of people, a quick understanding of scientific articles will help the medical community. Automatic text summarization makes this possible. Materials and Methods: In this study, a recurrent neural network-based extractive summarization is proposed. The extractive method identifies the informative parts of the text. Recurrent neural network is very powerful for analyzing sequences such as text. The proposed method has three phases: sentence encoding, sentence ranking, and summary generation. To improve the performance of the summarization system, a coreference resolution procedure is used. Coreference resolution identifies the mentions in the text that refer to the same entity in the real world. This procedure helps to summarization process by discovering the central subject of the text. Results: The proposed method is evaluated on the COVID-19 research articles extracted from the CORD-19 dataset. The results show that the combination of using recurrent neural network and coreference resolution embedding vectors improves the performance of the summarization system. The Proposed method by achieving the value of ROUGE1-recall 0.53 demonstrates the improvement of summarization performance by using coreference resolution embedding vectors in the RNN-based summarization system. Conclusion: In this study, coreference information is stored in the form of coreference embedding vectors. Jointly use of recurrent neural network and coreference resolution results in an efficient summarization system.

Evidence-Based Recommender System for a COVID-19 Publication Analytics Service

The rapid growth of COVID-19 publications has driven clinical researchers and healthcare professionals in pursuit to reduce the knowledge gap on reliable information for effective pandemic solutions. The manual task of retrieving high-quality publications based on the evidence pyramid levels, however, presents a major bottleneck in researchers' workflows. In this paper, we propose an “evidence-based” recommender system namely, KnowCOVID-19 that utilizes an edge computing service to integrate recommender modules for data analytics using end-user thin-clients. The edge computing service features chatbot-based web interface that handles a given COVID-19 publication dataset using two recommender system modules: (i) evidence-based filtering that observes domain specific topics across the literature and classifies the filtered information according to a clinical category, and (ii) social filtering that allows diverse experts with similar objectives to collaborate via a “social plane” to jointly find answers to critical clinical questions to fight the pandemic. We compare the Domain-specific Topic Model (DSTM) used in our evidence-based filtering with state-of-the-art models considering the CORD-19 dataset (a COVID-19 publication archive) and show improved generalization effectiveness as well as knowledge pattern query effectiveness. In addition, we conduct a comparison study between a manual literature review process and the KnowCOVID-19 augmented process, and evaluate the benefits of our information retrieval techniques over important queries provided by COVID-19 clinical experts.

Sistemas de recuperación de información implementados a partir de CORD-19: herramientas clave en la gestión de la información sobre COVID-19

La investigación sobre el coronavirus ha generado una producción de documentos científicos extraordinaria. Su tratamiento y asimilación por parte de la comunidad científica ha necesitado de la ayuda de sistemas de recuperación de información diseñados específicamente. Algunas de las principales instituciones mundiales dedicadas a la lucha contra la pandemia han desarrollado el conjunto de datos CORD-19 que destaca sobre otros proyectos de similar naturaleza. Los documentos recopilados en esta fuente han sido procesados por distintas herramientas de recuperación de información, a veces prototipos o sistemas que ya estaban implementados. Se ha analizado la tipología y características principales de estos sistemas concluyendo que hay tres grandes categorías no excluyentes entre ellas: búsqueda terminológica, visualización de información y procesamiento de lenguaje natural. Destaca enormemente que la gran mayoría de ellos emplean preferentemente tecnologías de búsqueda semántica con el objeto de facilitar la adquisición de conocimiento s los investigadores y ayudarlas en su ingente tarea. La crisis provocada por la pandemia ha sido aprovechada por los buscadores semánticos para encontrar su sitio.

Dental Risks and Precautions during COVID-19 Pandemic: A Systematic Review.

ABSTRACTObjective:To provide dentists and dental team with insights concerning risk and precautions during COVID-19 outbreak crisis as it would help in decision making among dental community.Materials and Methods:A comprehensive review of all English and non-English articles was carried out using the available CORD-19 dataset with MEDLINE via PubMed, Cochrane library, Google Scholar and ScienceDirect databases. The study included all articles that matched the search terms.Results:A total of 353 were retrieved, of which 13 articles were reviewed comprehensively. Studies included in this systematic review emphasized on reinforcing strict infection control measures and minimizing human-to-human contact during COVID-19 outbreak. All surfaces in operatory room including waiting area need adequate ventilation and disinfection. Dental patients need to be screened using COVID-19 targeted questions as well as measuring their body temperature. Restricting dental treatments to only emergency cases and rescheduling all routine visits is advised with careful attention to minimize aerosol generation and following highest level of personal protection when treating COVID-19 confirmed cases. Dental offices need to establish a standard protocol of case reporting and referral to other well-prepared facilities. Lastly, online platforms are beneficial tools in providing psychological support to distressed dentists, dental team and dental patients and educating public during COVID-19 crisis.Conclusion:Dental team need to follow strict infection control measures and minimize aerosol generation during COVID-19 outbreak. It is the responsibility of dental care workers to keep themselves informed and ensure safety and control transmission within dental facilities. Further research is required.