More Web Proxy on the site http://driver.im/

research-article

MultiGBS: : A multi-layer graph approach to biomedical summarization

Authors:

Ensieh Davoodijam,

Nasser Ghadiri,

Maryam Lotfi Shahreza,

Fabio RinaldiAuthors Info & Claims

Volume 116, Issue C

https://doi.org/10.1016/j.jbi.2021.103706

Published: 01 April 2021 Publication History

Graphical abstract

Display Omitted

Highlights

•

A multi-layer graph-based biomedical text summarizer is presented as MultiGBS.

•

MultiGBS covers semantic, word and co-reference relationships.

•

A MultiRank algorithm works on three different layers to rank all the sentences.

•

Extensive evaluation by ROUGE and BertScore shows increased F-measure values.

Abstract

Automatic text summarization methods generate a shorter version of the input text to assist the reader in gaining a quick yet informative gist. Existing text summarization methods generally focus on a single aspect of text when selecting sentences, causing the potential loss of essential information. In this study, we propose a domain-specific method that models a document as a multi-layer graph to enable multiple features of the text to be processed at the same time. The features we used in this paper are word similarity, semantic similarity, and co-reference similarity, which are modelled as three different layers. The unsupervised method selects sentences from the multi-layer graph based on the MultiRank algorithm and the number of concepts. The proposed MultiGBS algorithm employs UMLS and extracts the concepts and relationships using different tools such as SemRep, MetaMap, and OGER. Extensive evaluation by ROUGE and BERTScore shows increased F-measure values.

References

[1]

Home - - NCBI, (n.d.). https://www.ncbi.nlm.nih.gov/pubmed/ (accessed December 15, 2017).

[2]

M.S. Simpson, D. Demner-Fushman, Biomedical Text Mining: A Survey of Recent Progress, in: Min. Text Data, Springer US, Boston, MA, 2012, pp. 465–517,.

[3]

E. Lloret, M. Palomar, Text summarisation in progress: A literature review, Artif. Intell. Rev. 37 (2012) 1–41,.

Digital Library

[4]

M. Gambhir, V. Gupta, M. Gambhir, V. Gupta, Recent automatic text summarization techniques: a survey, Artif. Intell. Rev. 47 (2017) 1–66,.

Digital Library

[5]

Y. Liu, T. Safavi, A. Dighe, D. Koutra, Graph Summarization Methods and Applica-tions: A Survey, ACM Comput. Surv. 51 (2018),.

Digital Library

[6]

L. Plaza, A. Díaz, P. Gervás, A semantic graph-based approach to biomedical summarisation, Artif. Intell. Med. 53 (2011) 1–14,.

Digital Library

[7]

Unified Medical Language System (UMLS), (n.d.). https://www.nlm.nih.gov/research/umls/ (accessed April 25, 2019).

[8]

O. Bodenreider, The Unified Medical Language System (UMLS): integrating biomedical terminology, Nucleic Acids Res. 32 (2004) 267D–270D,.

[9]

MetaMap - A Tool For Recognizing UMLS Concepts in Text, (n.d.). https://metamap.nlm.nih.gov/ (accessed April 25, 2019).

[10]

M. Basaldella, L. Furrer, C. Tasso, F. Rinaldi, Entity recognition in the biomedical domain using a hybrid approach, J. Biomed. Semantics 8 (2017) 51,.

[11]

C. Rahmede, J. Iacovacci, A. Arenas, G. Bianconi, Centralities of nodes and influences of layers in large multiplex networks, J. Complex Networks 6 (2018) 733–752,.

[12]

C.Y. Lin, Rouge: A package for automatic evaluation of summaries, in: Proc. Work. Text Summ. Branches out (WAS 2004). (2004) 25–26.

[13]

K. Ganesan, ROUGE 2.0: Updated and Improved Measures for Evaluation of Summarization Tasks, (2018). http://arxiv.org/abs/1803.01937 (accessed September 12, 2020).

[14]

T. Zhang, V. Kishore, F. Wu, K.Q. Weinberger, Y. Artzi, BERTScore: Evaluating Text Generation with BERT, (2019). http://arxiv.org/abs/1904.09675 (accessed December 23, 2019).

[15]

K. Spärck Jones, Automatic summarising: The state of the art, Inf. Process. Manag. 43 (2007) 1449–1481,.

Digital Library

[16]

R. Mishra, J. Bian, M. Fiszman, C.R. Weir, S. Jonnalagadda, J. Mostafa, G. Del Fiol, Text summarization in the biomedical domain: A systematic review of recent research, J. Biomed. Inform. 52 (2014) 457–467,.

Digital Library

[17]

V. McCargar, Statistical Approaches to Automatic Text Summarization, Bull. Am. Soc. Inf. Sci. Technol. 30 (2005) 21–25,.

[18]

Y. Ko, J. Seo, An effective sentence-extraction technique using contextual information and statistical approaches for text summarization, Pattern Recogn. Lett. 29 (2008) 1366–1371,.

Digital Library

[19]

H.P. Edmundson, New methods in automatic extracting, J. Assoc. Comput. Mach. 16 (1969) 264–285,.

Digital Library

[20]

S. Harabagiu, F. Lacatusu, Topic themes for multi-document summarization, in: ACM Press, New York, New York, USA, 2005. https://doi.org/10.1145/1076034.1076071.

[21]

J. Kupiec, J. Pedersen, F. Chen, Trainable document summarizer, in: SIGIR Forum (ACM Spec. Interes. Gr. Inf. Retrieval), ACM, 1995, pp. 68–73,.

Digital Library

[22]

J.M. Conroy, D.P. O’leary, Text summarization via hidden Markov models, in: SIGIR Forum (ACM Spec. Interes. Gr. Inf. Retrieval), 2001, pp. 406–407,.

Digital Library

[23]

C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, G. Hullender, Learning to rank using gradient descent, in: ICML 2005 - Proc. 22nd Int. Conf. Mach. Learn., 2005, pp. 89–96,.

Digital Library

[24]

J.D. Schlesinger, J.D. Schlesinger, M.E. Okurowski, J.M. Conroy, D.P. O’Leary, A. Taylor, J. Hobbs, H.T. Wilson, Understanding Machine Performance in the Context of Human Performance for Multi-Document Summarization, (2002). https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.5.23 (accessed December 10, 2019).

[25]

R. Nallapati, B. Zhou, C.N. dos santos, C. Gulcehre, B. Xiang, Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond, (2016). http://arxiv.org/abs/1602.06023 (accessed May 9, 2018).

[26]

J. Cheng, M. Lapata, Neural summarization by extracting sentences and words, in: 54th Annu. Meet. Assoc. Comput. Linguist. ACL 2016 - Long Pap., Association for Computational Linguistics (ACL), 2016, pp. 484–494,.

[27]

Z. Cao, F. Wei, S. Li, W. Li, M. Zhou, H. Wang, Learning summary prior representation for extractive summarization, in: ACL-IJCNLP 2015 - 53rd Annu. Meet. Assoc. Comput. Linguist. 7th Int. Jt. Conf. Nat. Lang. Process. Asian Fed. Nat. Lang. Process. Proc. Conf., Association for Computational Linguistics (ACL), 2015, pp. 829–833,.

[28]

S. Narayan, J. Maynez, J. Adamek, D. Pighin, B. Bratanič, R. Mcdonald, Stepwise Extractive Summarization and Planning with Structured Transformers, n.d. https://github (accessed November 27, 2020).

[29]

W.C. Mann, S.A. Thompson, Rhetorical Structure Theory: Toward a functional theory of text organization, Text 8 (1988) 243–281,.

[30]

D. Marcu, D. Marcu, Discourse Trees Are Good Indicators of Importance in Text, Adv. Autom. TEXT Summ. (1999) 123--136. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.46.8292 (accessed December 10, 2019).

[31]

A.U. Khan, S. Khan, W. Mahmood, MRST : A New Technique for Information, Second World Enformatika Conf. WEC’05. (2007) 639–642. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.105.5232&rep=rep1&type=pdf (accessed April 30, 2020).

[32]

G. Erkan, D.R. Radev, LexRank: Graph-based lexical centrality as salience in text summarization, J. Artif. Intell. Res. 22 (2004) 457–479,.

[33]

K. Sarkar, K. Saraf, A. Ghosh, Improving graph based multidocument text summarization using an enhanced sentence similarity measure, in: 2015 IEEE 2nd Int. Conf. Recent Trends Inf. Syst. ReTIS 2015 - Proc., 2015, pp. 359–365,.

[34]

R. Mihalcea, P. Tarau, TextRank: Bringing order into texts, Proc. EMNLP 85 (2004) 404–411,.

Digital Library

[35]

E. Baralis, P. Torino, L. Cagliero, A. Fiori, Multi-document summarization exploiting frequent itemsets Categories and Subject Descriptors, in: Proc. 27th Annu. ACM Symp. Appl. Comput., 2012, pp. 782–786.

[36]

E. Baralis, L. Cagliero, A. Fiori, P. Garza, MWI-Sum: A Multilingual Summarizer Based on Frequent Weighted Itemsets, ACM Trans. Inf. Syst. 34 (2015) 1–35,.

Digital Library

[37]

J.-P. Qiang, P. Chen, W. Ding, F. Xie, X. Wu, Knowle dge-Base d Systems Multi-document summarization using closed patterns, Knowledge-Based Syst. 99 (2016) 28–38,.

Digital Library

[38]

G.-W. Kim, D.-H. Lee, Personalised health document summarisation exploiting Unified Medical Language System and topic-based clustering for mobile healthcare, 16555151772298 J. Inf. Sci. (2017),.

Digital Library

[39]

L. Plaza, Comparing different knowledge sources for the automatic summarization of biomedical literature, J. Biomed. Inform. 52 (2014) 319–328,.

Digital Library

[40]

M. Moradi, N. Ghadiri, Quantifying the informativeness for biomedical literature summarization: An itemset mining method, Comput. Methods Programs Biomed. 146 (2017) 77–89,.

[41]

M. Nasr Azadani, N. Ghadiri, E. Davoodijam, Graph-based biomedical text summarization: An itemset mining and sentence clustering approach, J. Biomed. Inform. 84 (2018) 42–58,.

Digital Library

[42]

L.H. Reeve, H. Han, A.D. Brooks, BioChain: lexical chaining methods for biomedical text summarization, Sac. (2006) 180–184.

[43]

H.D. Menéndez, L. Plaza, D. Camacho, A genetic graph-based clustering approach to biomedical summarization, in: Proc. 3rd Int. Conf. Web Intell. Min. Semant. - WIMS ’13, 2013, p. 1,.

Digital Library

[44]

L. Tang, X. Wang, H. Liu, Community detection via heterogeneous interaction analysis, Data Min. Knowl. Discov. 25 (2012) 1–33,.

Digital Library

[45]

L. Furrer, A. Jancso, N. Colic, F. Rinaldi, OGER++: hybrid multi-type entity recognition, J. Cheminform. 11 (2019) 7,.

[46]

G. Kondrak, N-Gram Similarity and Distance, in: Springer, Berlin, Heidelberg, 2005: pp. 115–126. https://doi.org/10.1007/11575832_13.

[47]

The Stanford Natural Language Processing Group, (n.d.). https://nlp.stanford.edu/projects/coref.shtml (accessed November 28, 2020).

[48]

T.C. Rindflesch, M. Fiszman, The interaction of domain knowledge and linguistic structure in natural language processing: Interpreting hypernymic propositions in biomedical text, J. Biomed. Inform. 36 (2003) 462–477,.

Digital Library

[49]

H. Kilicoglu, G. Rosemblat, M. Fiszman, T.C. Rindflesch, Sortal anaphora resolution to enhance relation extraction from biomedical literature, BMC Bioinf. 17 (2016) 163,.

[50]

L. Plaza, J. Carrillo-de-Albornoz, Evaluating the use of different positional strategies for sentence selection in biomedical literature summarization, BMC Bioinf. 14 (2013) 71,.

[51]

SciPy.org — SciPy.org, (n.d.). https://www.scipy.org/ (accessed September 12, 2020).

[52]

J. Devlin, M.-W. Chang, K. Lee, K.T. Google, A.I. Language, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, n.d. https://github.com/tensorflow/tensor2tensor (accessed March 28, 2020).

[53]

Document Understanding Conferences, (n.d.). https://duc.nist.gov/ (accessed December 1, 2019).

[54]

Text Analysis Conference (TAC), (n.d.). https://tac.nist.gov/ (accessed December 1, 2019).

[55]

G. Balikas, A. Krithara, I. Partalas, G. Paliouras, BioASQ: A Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering, in: Springer, Cham, 2015: pp. 26–39. https://doi.org/10.1007/978-3-319-24471-6_3.

[56]

P. Vision, G. Paliouras, A. Krithara, BioASQ, (n.d.).

[57]

L. Reeve, H. Han, A.D. Brooks, BioChain, Proc. 2006 ACM Symp. Appl. Comput. - SAC ’06. (2006) 180. https://doi.org/10.1145/1141277.1141317.

[58]

M. Moradi, CIBS: A biomedical text summarizer using topic-based sentence clustering, J. Biomed. Inform. 88 (2018) 53–61,.

[59]

E. Lloret, L. Plaza, A. Aker, The challenging task of summary evaluation: an overview, Lang. Resour. Eval. (2017) 1–48,.

Digital Library

[60]

C.-Y. Lin, Looking for a Few Good Metrics: Automatic Summarization Evaluation-How Many Samples Are Enough?, n.d. http://duc.nist.gov (accessed September 5, 2020).

[61]

D. Miller, Leveraging BERT for Extractive Text Summarization on Lectures, (2019). http://arxiv.org/abs/1906.04165 (accessed January 30, 2020).

[62]

J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, (2018). http://arxiv.org/abs/1810.04805 (accessed December 23, 2019).

[63]

D. Kim, J. Lee, C.H. So, H. Jeon, M. Jeong, Y. Choi, W. Yoon, M. Sung, J. Kang, A Neural Named Entity Recognition and Multi-Type Normalization Tool for Biomedical Text Mining, IEEE Access 7 (2019) 73729–73740,.

[64]

M. Bada, M. Eckert, D. Evans, K. Garcia, K. Shipley, D. Sitnikov, W.A. Baumgartner, K.B. Cohen, K. Verspoor, J.A. Blake, L.E. Hunter, Concept annotation in the CRAFT corpus, 2012. https://doi.org/10.1186/1471-2105-13-161.

Cited By

AbdelAziz NAli ANaguib SFayed L(2025)Clustering-based topic modeling for biomedical documents extractive text summarizationThe Journal of Supercomputing10.1007/s11227-024-06640-681:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06640-6
Gupta SSharaff ANagwani N(2023)Frequent item-set mining and clustering based ranked biomedical text summarizationThe Journal of Supercomputing10.1007/s11227-022-04578-179:1(139-159)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1007/s11227-022-04578-1

Index Terms

MultiGBS: A multi-layer graph approach to biomedical summarization

Index terms have been assigned to the content through auto-classification.

Recommendations

A topic modeled unsupervised approach to single document extractive text summarization
Abstract
Automatic Text Summarization (ATS) is an essential field in natural language processing that attempts to condense large text documents so that users can assimilate information quickly. It finds uses in medical document summarization, ...
Learning with fuzzy hypergraphs: A topical approach to query-oriented text summarization
Highlights
- Topical representations of the sentences of a corpus are computed based on a probabilistic topic model.
Abstract
Existing graph-based methods for extractive document summarization represent sentences of a corpus as the nodes of a graph in which edges depict relationships of lexical similarity between sentences. This approach fails to capture ...
Assessing shallow sentence scoring techniques and combinations for single and multi-document summarization

We investigate eighteen shallow sentence scoring techniques and ensemble strategies.Experiments were performed in several datasets for single- and multi-document task.Ensemble strategies lead to improvements over the individual scoring ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Journal of Biomedical Informatics

Journal of Biomedical Informatics Volume 116, Issue C

Apr 2021

224 pages

ISSN:1532-0464

Issue’s Table of Contents

Elsevier Inc.

Publisher

Elsevier Science

San Diego, CA, United States

Publication History

Published: 01 April 2021

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

AbdelAziz NAli ANaguib SFayed L(2025)Clustering-based topic modeling for biomedical documents extractive text summarizationThe Journal of Supercomputing10.1007/s11227-024-06640-681:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06640-6
Gupta SSharaff ANagwani N(2023)Frequent item-set mining and clustering based ranked biomedical text summarizationThe Journal of Supercomputing10.1007/s11227-022-04578-179:1(139-159)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1007/s11227-022-04578-1

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents