Word Embedding-Based Biomedical Text Summarization

Rouane, Oussama; Belhadef, Hacene; Bouakkaz, Mustapha

doi:10.1007/978-3-030-33582-3_28

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…With the increase in the vocabulary size in the biomedical domain, various representation and numerical feature-based similarity metrics such as word2vec are prominently employed in literary works [7,65,67,130].…”

Section: Rq3 What Are the Different Text Similarity Metrics Used In B...mentioning

confidence: 99%

“…Summarization techniques also employ corpusbased topic modeling techniques such as LDA, and LSA where an unsupervised approach to creating topics with related text and exhibiting better coherence. BTS works [7,109,111,116,118,125,130] show the use of the ROUGE metric to find the co-occurrence similarity between the text using n-grams grouping for its simplicity in capturing synonymous concepts. The study of the evolution of the vectorization techniques from feature-based to co-occurrence based approaches and their comparative study will support future works in the BTS.…”

Section: Rq3 What Are the Different Text Similarity Metrics Used In B...mentioning

confidence: 99%

“…Heuristic 2. As literature works [7,130,133] show that salient features and major findings of a complete article are found in the conclusion section, we are focusing on the conclusion section of scientific articles.…”

Section: Heuristicmentioning

confidence: 99%

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Survey on the Biomedical Text Summarization Techniques with an Emphasis on Databases, Techniques, Semantic Approaches, Classification Techniques, and Similarity Measures

et al. 2023

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Biomedical text summarization (BTS) is proving to be an emerging area of work and research with the need for sustainable healthcare applications such as evidence-based medicine practice (EBM) and telemedicine which help effectively support healthcare needs of the society. However, with the rapid growth in the biomedical literature and the diversities in its structure and resources, it is becoming challenging to carry out effective text summarization for better insights. The goal of this work is to conduct a comprehensive systematic literature review of significant and high-impact literary work in BTS with a deep understanding of its major artifacts such as databases, semantic similarity measures, and semantic enrichment approaches. In the systematic literature review conducted, we applied search filters to find high-impact literature in the biomedical text summarization domain from IEEE, SCOPUS, Elsevier, EBSCO, and PubMed databases. The systematic literature review (SLR) yielded 81 works; those were analyzed for qualitative study. The in-depth study of the literature shows the relevance and efficacy of the deep learning (DL) approach, context-aware feature extraction techniques, and their relevance in BTS. Biomedical question answering (BQA) system is one of the most popular applications of text summarizations for building self-sufficient healthcare systems and are pointing to future research directions. The review culminates in realization of a proposed framework for the BQA system MEDIQA with design of better heuristics for content screening, document screening, and relevance ranking. The presented framework provides an evidence-based biomedical question answering model and text summarizer that can lead to real-time evidence-based clinical support system to healthcare practitioners.

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Section: Rq3 What Are the Different Text Similarity Metrics Used In B...mentioning

confidence: 99%

Section: Rq3 What Are the Different Text Similarity Metrics Used In B...mentioning

confidence: 99%

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Survey on the Biomedical Text Summarization Techniques with an Emphasis on Databases, Techniques, Semantic Approaches, Classification Techniques, and Similarity Measures

et al. 2023

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“…CBOW predicts a target word based on its surrounding words. Skip-Gram predicts the surrounding words based on target word [12].…”

Section: Word Embeddingmentioning

confidence: 99%

Myanmar news summarization using different word representations

Lwin¹,

Nwet

2021

IJECE

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There is enormous amount information available in different forms of sources and genres. In order to extract useful information from a massive amount of data, automatic mechanism is required. The text summarization systems assist with content reduction keeping the important information and filtering the non-important parts of the text. Good document representation is really important in text summarization to get relevant information. Bag-of-words cannot give word similarity on syntactic and semantic relationship. Word embedding can give good document representation to capture and encode the semantic relation between words. Therefore, centroid based on word embedding representation is employed in this paper. Myanmar news summarization based on different word embedding is proposed. In this paper, Myanmar local and international news are summarized using centroid-based word embedding summarizer using the effectiveness of word representation approach, word embedding. Experiments were done on Myanmar local and international news dataset using different word embedding models and the results are compared with performance of bag-of-words summarization. Centroid summarization using word embedding performs comprehensively better than centroid summarization using bag-of-words.

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A systematic review of automatic text summarization for biomedical literature and EHRs

Wang

et al. 2021

Journal of the American Medical Informatics Association

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Objective Biomedical text summarization helps biomedical information seekers avoid information overload by reducing the length of a document while preserving the contents’ essence. Our systematic review investigates the most recent biomedical text summarization researches on biomedical literature and electronic health records by analyzing their techniques, areas of application, and evaluation methods. We identify gaps and propose potential directions for future research. Materials and Methods This review followed the PRISMA methodology and replicated the approaches adopted by the previous systematic review published on the same topic. We searched 4 databases (PubMed, ACM Digital Library, Scopus, and Web of Science) from January 1, 2013 to April 8, 2021. Two reviewers independently screened title, abstract, and full-text for all retrieved articles. The conflicts were resolved by the third reviewer. The data extraction of the included articles was in 5 dimensions: input, purpose, output, method, and evaluation. Results Fifty-eight out of 7235 retrieved articles met the inclusion criteria. Thirty-nine systems used single-document biomedical research literature as their input, 17 systems were explicitly designed for clinical support, 47 systems generated extractive summaries, and 53 systems adopted hybrid methods combining computational linguistics, machine learning, and statistical approaches. As for the assessment, 51 studies conducted an intrinsic evaluation using predefined metrics. Discussion and Conclusion This study found that current biomedical text summarization systems have achieved good performance using hybrid methods. Studies on electronic health records summarization have been increasing compared to a previous survey. However, the majority of the works still focus on summarizing literature.

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Word Embedding-Based Biomedical Text Summarization

Cited by 5 publications

References 18 publications

Survey on the Biomedical Text Summarization Techniques with an Emphasis on Databases, Techniques, Semantic Approaches, Classification Techniques, and Similarity Measures

Survey on the Biomedical Text Summarization Techniques with an Emphasis on Databases, Techniques, Semantic Approaches, Classification Techniques, and Similarity Measures

Myanmar news summarization using different word representations

A systematic review of automatic text summarization for biomedical literature and EHRs

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