Automatic Text Summarization of Biomedical Text Data: A Systematic Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Selection
2.1.1. Identification
2.1.2. Screening
2.1.3. Eligibility
- Studies or summarization tools that describe the evaluation component (metric) and method(s) used.
- Related Natural Language Processing techniques that can be used as text summarization methods (e.g., text mining, text generation).
2.1.4. Included
2.2. Summarization Factors
2.2.1. Input
2.2.2. Purpose
2.2.3. Output
2.2.4. Method
2.2.5. Evaluation Metrics
3. Results
3.1. Study Frequency According Geographical Distribution, Years, and Type of Publication
3.2. Study Frequency according to Summarization Factors
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Resource | Type of Input Text | Description |
---|---|---|
PubMed Central (PMC) | Biomedical Literature | More than 7 million full-text records of biomedical and life sciences journal literature at the U.S. National Institutes of Health’s National Library of Medicine (NIH/NLM). Open access [74] |
CRAFT: The Colorado Richly Annotated Full Text Corpus | Biomedical Literature | It is a manually annotated corpus consisting of 67 full-text biomedical journal articles. Each article is a member of the PMC subset. Open access [75,76] |
BioASQ Task-6a | Biomedical Literature | Contains 13 million citations from PubMed dataset, and each citation contains the title and abstract. Open access [77] |
PubMed | Biomedical Literature | Contains more than 34 million citations and abstracts supporting the search and retrieval of biomedical and life sciences literature. Open access [78] |
BioMed Central (BMC) | Biomedical Literature | 300 peer-reviewed journals in science, technology, engineering, and medicine. Open access [79] |
MEDLINE | Biomedical Literature | This database contains more than 29 million references to journal articles in life sciences with a concentration on biomedicine. The records are indexed with NLM Medical Subject Headings (MeSH). Open access [80,81] |
MEDIQA-AnS | Biomedical Literature | The dataset includes 156 questions with related documents as the answers for each. Each answer also has an extractive and an abstractive single-answer summaries and multidocument extractive and abstractive summary considering the information presented in all of the answers. [82] |
CORD-19: The Covid-19 Open Research Dataset | Biomedical Literature | It is a resource of scientific papers on COVID-19 and related historical coronavirus research. Open access [83] |
Radiology Reports | EHR | 41,066 real-world radiology reports from MedStar Georgetown University Hospital. Each report describes clinical findings about a specific diagnostic case, and an impression summary [40] |
DIAC-WoZ dataset | EHR | Clinical interviews designed to support the diagnosis of psychological distress conditions created by the Institute for Creative Technologies at the University of Southern California. Open access [84,85] |
NTUH-iMD | EHR | The corpus contains 258,050 discharge diagnoses obtained from the National Taiwan University Hospital Integrated Medical Database and the highlighted extractive summaries written by experienced doctors [47] |
Clinical trials | EHR | Dataset generation of 101,016 records usable for the summarization task from clinical trials. Open access [86] |
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Question | Purpose | |
---|---|---|
Q1 | What are the most prevalent methods used for text summarization in the biomedical domain? | To determine which techniques have been applied in text summarization in the biomedical domain. |
Q2 | What data types are used in text summarization in the biomedical domain? | To identify which types of text are most common, either single or multiple document. This will also allow us to assess the most frequently used application in biomedical literature or EHR. |
Q3 | Which areas in the biomedical field have applied text summarization techniques? | To find out which medical areas have implemented summarization methods. |
Q4 | What are the most common evaluation metrics of text summarization in the biomedical field? | To assess and identify suitable evaluation metrics to use when comparative studies are carried out on text summarization, mainly in the field of health care. |
Medical Keywords | Technical Keywords | Search Strategy |
---|---|---|
Biomedical OR biomedicine OR medical OR medicine OR healthcare OR health OR “patient care” OR clinical OR disease OR diseases OR therapy OR therapies OR treatment OR treatment OR diagnosis OR diagnoses OR diagnostic OR etiology | “Text summarization” OR “abstractive summarization” OR “extractive summarization” OR “abstractive text summarization” OR “extractive text summarization” OR “single document summarization” OR “multi-document summarization” OR “query-based summarization” OR “generic summarization” OR “hugging face” | All fields (Medical Keywords) AND All fields (Technical Keywords) AND (1 January 2014: 15 March 2022) |
Criteria | |
---|---|
Inclusion | Exclusion |
Complete records. Studies published in journals or at conferences, where the words obtained from the search strategy appear in the title and abstract. Studies that describe the evaluation component (metric) and method(s) used. | Studies not written in English, editorials, or opinion papers. Studies based on summarization techniques in fields other than the biomedical domain. Unavailable records. |
Parameters | Category | Frequency | |
---|---|---|---|
No. Studies | % | ||
Location | Eastern Africa | 1 | 1.09% |
Northern Africa | 3 | 3.26% | |
Africa | 4 | 4.35% | |
Eastern Asia | 22 | 23.91% | |
Southern Asia | 30 | 32.61% | |
Western Asia | 1 | 1.09% | |
Asia | 53 | 57.61% | |
Northern Europe | 1 | 1.09% | |
Southern Europe | 4 | 4.35% | |
Western Europe | 9 | 9.78% | |
Europe | 14 | 15.22% | |
North America | 16 | 17.39% | |
South America | 3 | 3.26% | |
America | 19 | 20.65% | |
Australia/Oceania | 2 | 2.17% | |
Year | 2014 | 3 | 3.26% |
2015 | 6 | 6.52% | |
2016 | 5 | 5.43% | |
2017 | 5 | 5.43% | |
2018 | 12 | 13.04% | |
2019 | 15 | 16.30% | |
2020 | 20 | 21.74% | |
2021 | 22 | 23.91% | |
2022 | 4 | 4.35% | |
Type of publication | Conference | 49 | 53.26% |
Journal | 43 | 46.74% |
Parameters | Category | Frequency | |
---|---|---|---|
No. Studies | % | ||
Input | Single-document (SD) | 25 | 89.29% |
Multiple-document (MD) | 1 | 3.57% | |
Single-multiple-document (SMD) | 2 | 7.14% | |
Biomedical literature (BL) | 20 | 71.43% | |
EHR (EHR) | 8 | 28.57% | |
Purpose | Query-based (QB) | 3 | 10.71% |
Generic (Ge) | 25 | 89.29% | |
Output | Extractive (Ex) | 21 | 75.00% |
Abstractive (Ab) | 6 | 21.43% | |
Extractive and abstractive (EA) | 1 | 3.57% | |
Method | Mathematical/Statistical (M/S) | 8 | 28.57% |
Machine Learning (ML) | 16 | 57.14% | |
Hybrid (Hy) | 4 | 14.29% | |
Evaluation Metric | Rouge (Rg) | 24 | 85.71% |
Rouge and others (R/O) | 2 | 7.14% | |
Other (O) | 2 | 7.14% | |
Human Evaluation | Human evaluation (HE) | 7 | 25.00% |
No human evaluation (NHE) | 21 | 75.00% |
Title | C/J | Loc. | Year | Input | Purpose | Out | Method (Best) | Metric (Best) | H. Evaluation |
---|---|---|---|---|---|---|---|---|---|
Ontology-Aware Clinical Abstractive Summarization [40] | C | USA | 2019 | SD, EHR: Radiology Reports | Ge | Ab | ML: pointer–generator based on Seq2Seq model | Rg 1:38.42 2:23.29 L:37.02 | HE: Radiologist (Readability, Accuracy, Completeness) |
Extractive Text Summarization using Ontology and Graph-Based Method [41] | C | Singapore | 2019 | SD, BL: Review papers | Ge | Ex | M/S: Graph-based method (PageRank) | Rg-P 1:25.46 L:23.61 | NHE |
Domain-Aware Abstractive Text Summarization for Medical Documents [42] | C | Spain | 2019 | SD, BL: abstracts from PubMed dataset | Ge | Ab | ML: deep-reinforced pointer–generator network | R/O- 1:42.43 2:21.59 L:36.89 TFIDF UMLS MeSH | NHE |
Knowledge-Infused Abstractive Summarization of Clinical Diagnostic Interviews: Framework Development Study [43] | J | USA | 2021 | SD, EHR: Diagnostic interviews by mental health professionals | QB: clinical diagnostic interviews | Ab | M/S: knowledge-infused abstractive summarization (KiAS) | Rg-L R:24.46 F1:32.57 | HE: Mental Health professionals (GQCC, GQUC, meaningful responses) |
Extractive summarization of clinical trial descriptions [22] | J | Germany | 2019 | SD, EHR: clinical trial descriptions from clinicaltrials.gov | Ge | Ex | ML: TextRank | Rg-L P:30.95 R:33.86 F1:30.03 | HE: Human reviewers (Contains all information, Helpfulness) |
Biomedical-domain pretrained language model for extractive summarization [44] | J | China | 2020 | SD, BL: titles and abstracts from PubMed dataset (Task 6a) | Ge | Ex | ML: domain-aware bidirectional language model (BioBERTSum) | Rg-F1 1:37.45 2:17.59 L:29.58 | NHE |
Deep contextualized embeddings for quantifying the informative content in biomedical text summarization [45] | J | Austria | 2020 | SD, BL: articles from BioMed Central database | Ge | Ex | Hy: deep bidirectional language model and clustering method (BERT-based, BERT-large) | Rg 1:75.04 2:33.12 | NHE |
CERC: an interactive content extraction, recognition, and construction tool for clinical and biomedical text [46] | J | England | 2020 | SD, BL: abstracts from Medline | Ge | Ex | ML: multistage algorithm (MINTS) | Rg 1:41.4 2:13.6 SU4:17.1 | NHE |
Evolutionary Algorithm based Ensemble Extractive Summarization for Developing Smart Medical System [6] | J | India | 2021 | SD, BL: PubMed and MEDLINE journal citations | Ge | Ex | Hy: Multiobjective Evolutionary Algorithm based on Decomposition (MOEAD) | Rg-F1 1:70.7 2:65.5 SU:47.9 | NHE |
Different approaches for identifying important concepts in probabilistic biomedical text summarization [7] | J | Iran | 2018 | SD, BL: Biomedical articles | Ge | Ex | M/S: Bayesian method | Rg 1:78.86 2:35.29 SU4:41.04 | NHE |
CIBS: A biomedical text summarizer using topic-based sentence clustering [31] | J | Iran | 2018 | SMD, BL: abstracts from PubMed and BioMed | Ge | Ex | ML: Clustering and Itemset mining (CIBs) | Rg 2:34.75 SU4:39.78 | NHE |
Modified Bidirectional Encoder Representations From Transformers Extractive Summarization Model for Hospital Information Systems Based on Character-Level Tokens (AlphaBERT): Development and Performance Evaluation [47] | J | Taiwan | 2020 | SD, EHR: diagnoses from National Taiwan University Hospital | Ge | Ex | ML: BERT-based structure with a two-stage training method (AlphaBERT) | Rg 1:76.9 2:61.0 L:75.1 | HE: Doctor feedback (Score) |
Summarization of biomedical articles using domain-specific word embeddings and graph ranking [48] | J | Austria | 2020 | SD, BL: articles from PubMed | Ge | Ex | Hy: domain-specific word embedding and graph-based model | Rg 1:76.87 2:34.91 | NHE |
MultiGBS: A multilayer graph approach to biomedical summarization [49] | J | Iran | 2021 | SD, BL: articles from BioMed Central | Ge | Ex | M/S: graph-based creation and sentence selection model (MultiGBS) | Rg/O Rg-F1 1:16.4 2:05.2 L:14.6 SU4:07.5 Bertscore F1:80.6 | NHE |
Quantifying the informativeness for biomedical literature summarization: An itemset mining method [38] | J | Iran | 2017 | SD, BL: Scientific papers | Ge | Ex | M/S: Itemset mining | Rg 1:75.83 2:33.81 SU4:38.89 | NHE |
Frequent itemsets as meaningful events in graphs for summarizing biomedical texts [50] | C | Iran | 2018 | SD, BL: scientific articles | Ge | Ex | M/S: Graph-based method | Rg 2:34.03 SU4:38.51 | NHE |
Nutri-bullets: Summarizing Health Studies by Composing Segments [51] | C | USA | 2021 | MD, BL: scientific abstracts from PubMed and ScienceDirect | Ge | Ab | ML: reinforcement learning (Blank Language Model—BLM) | O-Meteor Me:15.0 | HE: (Faithfulness, Relevance, Fluency) |
Self-supervised extractive text summarization for biomedical literature [52] | C | USA | 2021 | SD, BL: Radiation Therapy scientific articles from PubMed | Ge | Ex | ML: BERT | Rg-R 1:71.00 2:59.00 | NHE |
A Hybrid Multianswer Summarization Model for the Biomedical Question-Answering System [32] | C | Vietnam | 2021 | SMD, BL: Medical Question-Answer Summarization dataset (MEDIQA-AnS) | QB: Question-driven filtering phase | EA | ML: Denoising autoencoder and BART (Extractive Abstractive hybrid model - EAHS) | Rg-F1 1:30.00 2:22.00 L:25.00 | NHE |
Towards neural abstractive clinical trial text summarization with sequence to sequence models [23] | C | Kenya | 2019 | SD, EHR: clinical trial descriptions from clinical trials.gov | Ge | Ab | ML: Seq2Seq model with attention | Rg-F1 1:40.4 2:15.0 L:33.8 | NHE |
Extractive Text Summarization for COVID-19 Medical Records [53] | C | India | 2021 | SD, BL: COVID-19 research articles from PubMed, Microsoft Academic and WHO COVID-19 | Ge | Ex | ML: Generative Pre-Trained Transformer 2 (GPT-2) | Rg-F1 1:78.22 2:71.17 L:78.22 | NHE |
Fine-tuning the BERTSUMEXT model for Clinical Report Summarization [54] | C | India | 2020 | SD, EHR: clinical report summarization dataset | Ge | Ex | ML: Fine-tuned BERTSUMTEXT | Rg-F1 1:50.07 2:39.85 L:49.59 | HE: Doctor’s opinion |
A Hybrid Text Classification and Language Generation Model for Automated Summarization of Dutch Breast Cancer Radiology Reports [55] | C | Netherlands | 2020 | SD, EHR: Dutch breast cancer radiology reports | Ge | Ab | ML: encoder–decoder attention model (EDA) | Rg-F1 1:54.0 2:38.8 L:51.5 | HE: Radiologists (correctness, relevance, comprehensible) |
Query Specific Focused Summarization of Biomedical Journal Articles [56] | C | India | 2021 | SD, BL: articles from COVID-19 Open Research Dataset (CORD-19) | QB: User required information | Ex | M/S: Optimization and contextual method | Rg 1:47.61 2:19.62 L:44.74 | NHE |
Exploring Multi-Feature Optimization for Summarizing Clinical Trial Descriptions [24] | C | India | 2020 | SD, EHR: Clinical Trial Descriptions from Mendeley datasets | Ge | Ex | M/S: Multi Feature Optimization (MFO) | Rg-R 1:70.0 2:39.0 L:50.0 | NHE |
Automatic Text Summarization using Maximum Marginal Relevance for Health Ethics Protocol Document in Bahasa [57] | C | Indonesia | 2021 | SD, BL: Health research ethics protocol | Ge | Ex | M/S: Maximum Marginal Relevance (MMR) | Rg-4 P:34.0 R:71.0 F1:46.0 | NHE |
Finding Clinical Knowledge from MEDLINE Abstracts by Text Summarization Technique [58] | C | Thailand | 2018 | SD, BL: cervical cancer in clinical trials from MEDLINE abstracts | Ge | Ex | ML: BM25 term-weighting and text filtering techniques | O P:100.0 R:84.0 F1:91.0 | NHE |
Combining clustering and frequent item set mining to enhance biomedical text summarization [59] | J | USA | 2019 | SD, BL: articles from BioMed central database | Ge | Ex | Hy: clustering and frequent itemset meaning | Rg 1:23.84 2:08.71 SU4:11.45 | NHE |
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Chaves, A.; Kesiku, C.; Garcia-Zapirain, B. Automatic Text Summarization of Biomedical Text Data: A Systematic Review. Information 2022, 13, 393. https://doi.org/10.3390/info13080393
Chaves A, Kesiku C, Garcia-Zapirain B. Automatic Text Summarization of Biomedical Text Data: A Systematic Review. Information. 2022; 13(8):393. https://doi.org/10.3390/info13080393
Chicago/Turabian StyleChaves, Andrea, Cyrille Kesiku, and Begonya Garcia-Zapirain. 2022. "Automatic Text Summarization of Biomedical Text Data: A Systematic Review" Information 13, no. 8: 393. https://doi.org/10.3390/info13080393
APA StyleChaves, A., Kesiku, C., & Garcia-Zapirain, B. (2022). Automatic Text Summarization of Biomedical Text Data: A Systematic Review. Information, 13(8), 393. https://doi.org/10.3390/info13080393