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Academic literature on the topic 'Biomedical summarization'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Biomedical summarization"

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Chaves, Andrea, Cyrille Kesiku, and Begonya Garcia-Zapirain. "Automatic Text Summarization of Biomedical Text Data: A Systematic Review." Information 13, no. 8 (August 19, 2022): 393. http://dx.doi.org/10.3390/info13080393.

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In recent years, the evolution of technology has led to an increase in text data obtained from many sources. In the biomedical domain, text information has also evidenced this accelerated growth, and automatic text summarization systems play an essential role in optimizing physicians’ time resources and identifying relevant information. In this paper, we present a systematic review in recent research of text summarization for biomedical textual data, focusing mainly on the methods employed, type of input data text, areas of application, and evaluation metrics used to assess systems. The survey was limited to the period between 1st January 2014 and 15th March 2022. The data collected was obtained from WoS, IEEE, and ACM digital libraries, while the search strategies were developed with the help of experts in NLP techniques and previous systematic reviews. The four phases of a systematic review by PRISMA methodology were conducted, and five summarization factors were determined to assess the studies included: Input, Purpose, Output, Method, and Evaluation metric. Results showed that 3.5% of 801 studies met the inclusion criteria. Moreover, Single-document, Biomedical Literature, Generic, and Extractive summarization proved to be the most common approaches employed, while techniques based on Machine Learning were performed in 16 studies and Rouge (Recall-Oriented Understudy for Gisting Evaluation) was reported as the evaluation metric in 26 studies. This review found that in recent years, more transformer-based methodologies for summarization purposes have been implemented compared to a previous survey. Additionally, there are still some challenges in text summarization in different domains, especially in the biomedical field in terms of demand for further research.
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Byrnes, Patrick D., and William Evan Higgins. "Efficient Bronchoscopic Video Summarization." IEEE Transactions on Biomedical Engineering 66, no. 3 (March 2019): 848–63. http://dx.doi.org/10.1109/tbme.2018.2859322.

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Guo, Yue, Wei Qiu, Yizhong Wang, and Trevor Cohen. "Automated Lay Language Summarization of Biomedical Scientific Reviews." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 160–68. http://dx.doi.org/10.1609/aaai.v35i1.16089.

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Health literacy has emerged as a crucial factor in making appropriate health decisions and ensuring treatment outcomes. However, medical jargon and the complex structure of professional language in this domain make health information especially hard to interpret. Thus, there is an urgent unmet need for automated methods to enhance the accessibility of the biomedical literature to the general population. This problem can be framed as a type of translation problem between the language of healthcare professionals, and that of the general public. In this paper, we introduce the novel task of automated generation of lay language summaries of biomedical scientific reviews, and construct a dataset to support the development and evaluation of automated methods through which to enhance the accessibility of the biomedical literature. We conduct analyses of the various challenges in performing this task, including not only summarization of the key points but also explanation of background knowledge and simplification of professional language. We experiment with state-of-the-art summarization models as well as several data augmentation techniques, and evaluate their performance using both automated metrics and human assessment. Results indicate that automatically generated summaries produced using contemporary neural architectures can achieve promising quality and readability as compared with reference summaries developed for the lay public by experts (best ROUGE-L of 50.24 and Flesch-Kincaid readability score of 13.30). We also discuss the limitations of the current effort, providing insights and directions for future work.
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Plaza, Laura, Mark Stevenson, and Alberto Díaz. "Resolving ambiguity in biomedical text to improve summarization." Information Processing & Management 48, no. 4 (July 2012): 755–66. http://dx.doi.org/10.1016/j.ipm.2011.09.005.

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Shang, Yue, Yanpeng Li, Hongfei Lin, and Zhihao Yang. "Enhancing Biomedical Text Summarization Using Semantic Relation Extraction." PLoS ONE 6, no. 8 (August 26, 2011): e23862. http://dx.doi.org/10.1371/journal.pone.0023862.

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Saini, Naveen, Sriparna Saha, Pushpak Bhattacharyya, and Himanshu Tuteja. "Textual Entailment--Based Figure Summarization for Biomedical Articles." ACM Transactions on Multimedia Computing, Communications, and Applications 16, no. 1s (April 28, 2020): 1–24. http://dx.doi.org/10.1145/3357334.

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Davoodijam, Ensieh, Nasser Ghadiri, Maryam Lotfi Shahreza, and Fabio Rinaldi. "MultiGBS: A multi-layer graph approach to biomedical summarization." Journal of Biomedical Informatics 116 (April 2021): 103706. http://dx.doi.org/10.1016/j.jbi.2021.103706.

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Du, Yongping, Qingxiao Li, Lulin Wang, and Yanqing He. "Biomedical-domain pre-trained language model for extractive summarization." Knowledge-Based Systems 199 (July 2020): 105964. http://dx.doi.org/10.1016/j.knosys.2020.105964.

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Wu, Xiaofang, Zhihao Yang, ZhiHeng Li, Hongfei Lin, and Jian Wang. "Disease Related Knowledge Summarization Based on Deep Graph Search." BioMed Research International 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/428195.

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The volume of published biomedical literature on disease related knowledge is expanding rapidly. Traditional information retrieval (IR) techniques, when applied to large databases such as PubMed, often return large, unmanageable lists of citations that do not fulfill the searcher’s information needs. In this paper, we present an approach to automatically construct disease related knowledge summarization from biomedical literature. In this approach, firstly Kullback-Leibler Divergence combined with mutual information metric is used to extract disease salient information. Then deep search based on depth first search (DFS) is applied to find hidden (indirect) relations between biomedical entities. Finally random walk algorithm is exploited to filter out the weak relations. The experimental results show that our approach achieves a precision of 60% and a recall of 61% on salient information extraction forCarcinoma of bladderand outperforms the method of Combo.
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S. Almasoud, Ahmed, Siwar Ben Haj Hassine, Fahd N. Al-Wesabi, Mohamed K. Nour, Anwer Mustafa Hilal, Mesfer Al Duhayyim, Manar Ahmed Hamza, and Abdelwahed Motwakel. "Automated Multi-Document Biomedical Text Summarization Using Deep Learning Model." Computers, Materials & Continua 71, no. 3 (2022): 5799–815. http://dx.doi.org/10.32604/cmc.2022.024556.

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Dissertations / Theses on the topic "Biomedical summarization"

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Reeve, Lawrence H. Han Hyoil. "Semantic annotation and summarization of biomedical text /." Philadelphia, Pa. : Drexel University, 2007. http://hdl.handle.net/1860/1779.

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Jaykumar, Nishita. "ResQu: A Framework for Automatic Evaluation of Knowledge-Driven Automatic Summarization." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1464628801.

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Tsatsaronis, George. "An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-202687.

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This article provides an overview of the first BioASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BioASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies.
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Tsatsaronis, George. "An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition." BioMed Central, 2015. https://tud.qucosa.de/id/qucosa%3A29496.

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This article provides an overview of the first BioASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BioASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies.
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Heng-HuiLiu and 劉恒惠. "Intelligent Biomedical Information Summarization for Omic Study." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/14042056071648837233.

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Gan, Sheng-Xuan, and 甘昇玄. "A Summarization System for Gene Relations in Biomedical Literatures." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/55977456912415319605.

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碩士
國立成功大學
資訊工程學系碩博士班
92
After creating thirty hundred million DNA sequences’ database by biologists, it needs the high speed process to analyze the vast amounts of data . This paper proposes an summarization system which summarizes a query gene’s related information in biomedical literatures. This system combines three techniques :data mining methodology, natural language processing , finite state machine and consists of three process steps. First ,utilize the author’s writing habits in biomedical literatures and data mining method to extract the candidate related genes , functions and diseases. Second , tag the part-of-speech of sentences’ tokens and simplify the sentence pattern. Third , use finite state machine to extract summary sentences that describe the relation between the query gene and other genes, functions or diseases. 　　Finally , the summary information is integrated to : 1. Information about related genes. 2.Information about related functions. 3. Information about related diseases.This system provides information to help researches about biological pathway ,protein-protein interaction , human cancer gene and gene’s function and increase the process of medical research.
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Shang, Tsung-Cheng, and 尚宗承. "Applying Summarization System and Information Distance Method to Biomedical Question Answering System." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/76259362842150385968.

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碩士
國立臺灣師範大學
資訊工程學系
102
The study takes Alzheimer’s disease as a subject to implement a biomedical question answering system. The purpose in the thesis is to employ both the properties of a summarization system and an information distance method to the question answering system. The machine learning techniques are also applied, attempting to find out a correct answer from the related literature and background knowledge. 　　The test data is composed of four sets of test documents. Each set includes one document, ten questions and five answer options per question. For each question, there is only one correct answer from the multiple choices. The study also utilizes the background collections from the articles of Medical Literature Analysis and Retrieval System Online, called Medline, and Massachusetts Alzheimer’s Disease Research Center. 　　In the thesis, several different approaches are adopted towards developing an effective question answering system. The first approach is related to methods used in the study of Hou and Tsai in 2014.In this study, the previous approach is extended using the summarization technique to obtain the important information. The second approach is related to the concept of the information distance. The thesis proposes that the information distance between the question and the corresponding correct answer must be smaller than the distances between the question and the other incorrect answers. Furthermore, the concept of the information distance is adapted to fit the characteristics of QA4MRE. Besides, two other techniques, TFIDF computation and the query expansion, are also used in the second approach. 　　Finally, from the experiment of the first approach, it shows that the relevance between the literatures in background knowledge and the question in the test set is not high enough. We observe that, if we make a summary of literatures in background knowledge that may include too many noises among, we can effectively capture the important information needed. From the experiment by the second method, we observe that, if we increase the number of “Question Focus,” we can effectively improve the accuracy of the system. 　　In summary, both summarization and information distance methods are applied to the biomedical question answering system in the study. The experiments show that summarizing the literatures in background knowledge and applying the information distance method can yield good results.
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Book chapters on the topic "Biomedical summarization"

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Rouane, Oussama, Hacene Belhadef, and Mustapha Bouakkaz. "Word Embedding-Based Biomedical Text Summarization." In Advances in Intelligent Systems and Computing, 288–97. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33582-3_28.

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Shi, Zhongmin, Gabor Melli, Yang Wang, Yudong Liu, Baohua Gu, Mehdi M. Kashani, Anoop Sarkar, and Fred Popowich. "Question Answering Summarization of Multiple Biomedical Documents." In Advances in Artificial Intelligence, 284–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72665-4_25.

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Kaykobad Reza, Md, Rifat Rubayatul Islam, Sadik Siddique, Md Mostofa Akbar, and M. Sohel Rahman. "Automatic Summarization of Scientific Articles from Biomedical Domain." In Proceedings of International Joint Conference on Computational Intelligence, 591–602. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3607-6_47.

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Gupta, Supriya, Aakanksha Sharaff, and Naresh Kumar Nagwani. "Biomedical Text Summarization: A Graph-Based Ranking Approach." In Advances in Intelligent Systems and Computing, 147–56. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2008-9_14.

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Nasr Azadani, Mozhgan, and Nasser Ghadiri. "Evaluating Different Similarity Measures for Automatic Biomedical Text Summarization." In Advances in Intelligent Systems and Computing, 305–14. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76348-4_30.

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Mallick, Chirantana, and Asit Kumar Das. "Hybridization of Fuzzy Theory and Nature-Inspired Optimization for Medical Report Summarization." In Nature-Inspired Optimization Methodologies in Biomedical and Healthcare, 147–74. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17544-2_7.

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Yoo, Illhoi, Xiaohua Hu, and Il-Yeol Song. "A Coherent Biomedical Literature Clustering and Summarization Approach Through Ontology-Enriched Graphical Representations." In Data Warehousing and Knowledge Discovery, 374–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11823728_36.

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Rouane, Oussama, Hacene Belhadef, and Mustapha Bouakkaz. "A New Biomedical Text Summarization Method Based on Sentence Clustering and Frequent Itemsets Mining." In Smart Innovation, Systems and Technologies, 144–52. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21005-2_14.

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Gupta, Supriya, Aakanksha Sharaff, and Naresh Kumar Nagwani. "Biomedical Text Summarization Based on the Itemset Mining Approach." In Advances in Data Mining and Database Management, 140–52. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8061-5.ch007.

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The expanding amount of text-based biomedical information has prompted mining valuable or intriguing frequent patterns (words/terms) from extremely massive content, which is still a very challenging task. In the chapter, the authors have conceived a practical methodology for text mining dependent on the frequent item sets. This chapter presents a strategy utilizing item set mining graph-based summarization for summing up biomedical literature. They address the difficulties of recognizing important subjects or concepts in the given biomedical document text and display the relations between the strings by choosing the high pertinent lines from biomedical literature using apriori itemset mining algorithm. This method utilizes essential criteria to distinguish the significant concepts, events, for example, the fundamental subjects of the input record. These sentences are determined as exceptionally educational, applicable, and chosen to create the final summary.
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Clevert, Djork-Arné, and Axel Rasche. "The Affymetrix GeneChip® Microarray Platform." In Handbook of Research on Systems Biology Applications in Medicine, 251–61. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-076-9.ch014.

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Readers shall find a quick introduction with recommendations into the preprocessing of Affymetrix GeneChip® microarrays. In the rapidly growing field of microarrays, gene expression, especially the Affymetrix GeneChip arrays, is an established technology present on the market for over ten years. Used in biomedical research, the mass of information demands statistics for its analysis. Here we present the particular design of GeneChip arrays, where much research has already been invested and some validation resources for the comparison of the methods are available. For a basic understanding of the preprocessing, we emphasize the steps, namely: background correction, normalization, perfect match correction, summarization, and couple these with alternative probe-gene assignments. Combined with a recommendation of successful methods a first use of the new technology becomes possible.
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Conference papers on the topic "Biomedical summarization"

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Yoo, Illhoi, Xiaohua Hu, and Il-Yeol Song. "Integrating biomedical literature clustering and summarization approaches using biomedical ontology." In the 1st international workshop. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1183535.1183545.

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Reeve, Lawrence H., Hyoil Han, Saya V. Nagori, Jonathan C. Yang, Tamara A. Schwimmer, and Ari D. Brooks. "Concept frequency distribution in biomedical text summarization." In the 15th ACM international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1183614.1183701.

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Rai, Akshara, Suyash Sangwan, Tushar Goel, Ishan Verma, and Lipika Dey. "Query Specific Focused Summarization of Biomedical Journal Articles." In 16th Conference on Computer Science and Intelligence Systems. IEEE, 2021. http://dx.doi.org/10.15439/2021f128.

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Xie, Tianyi, Yi Zhen, Tianqi Li, Chuqin Li, and Yaorong Ge. "Self-supervised extractive text summarization for biomedical literatures." In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI). IEEE, 2021. http://dx.doi.org/10.1109/ichi52183.2021.00091.

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Morales, Laura Plaza, Alberto Díaz Esteban, and Pablo Gervás. "Concept-graph based biomedical automatic summarization using ontologies." In the 3rd Textgraphs Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2008. http://dx.doi.org/10.3115/1627328.1627336.

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Fiszman, Marcelo, Thomas C. Rindflesch, and Halil Kilicoglu. "Abstraction summarization for managing the biomedical research literature." In the HLT-NAACL Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2004. http://dx.doi.org/10.3115/1596431.1596442.

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"A Practical Partial Parser for Biomedical Literature Summarization." In 1st International Workshop on Natural Language Understanding and Cognitive Science. SciTePress - Science and and Technology Publications, 2004. http://dx.doi.org/10.5220/0002678700750085.

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Chandu, Khyathi, Aakanksha Naik, Aditya Chandrasekar, Zi Yang, Niloy Gupta, and Eric Nyberg. "Tackling Biomedical Text Summarization: OAQA at BioASQ 5B." In BioNLP 2017. Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/w17-2307.

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Yamamoto, Y., and T. Takagi. "A Sentence Classification System for Multi Biomedical Literature Summarization." In 21st International Conference on Data Engineering Workshops (ICDEW'05). IEEE, 2005. http://dx.doi.org/10.1109/icde.2005.170.

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Menéndez, Héctor D., Laura Plaza, and David Camacho. "A genetic graph-based clustering approach to biomedical summarization." In the 3rd International Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2479787.2479807.

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