Medical documents processing for summary generation and keywords highlighting based on natural language processing and ontology graph descriptor approach

Alexander Dudko; Tatiana Endrjukaite; Yasushi Kiyoki

doi:10.1145/3151759.3151784

Medical documents processing for summary generation and keywords highlighting based on natural language processing and ontology graph descriptor approach

Alexander Dudko, Tatiana Endrjukaite, Yasushi Kiyoki

Faculty of Environment and Information Studies

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper a new method of data retrieval from free text documents in medical domain is proposed. Presented approach gives the document summary and highlights important keywords in the text to support further analysis of multiple medical documents. The document is processed with natural language processing techniques to find medical keywords and assign them to concepts in the medical ontology. These concepts contribute to higher levels in the hierarchy and build the document descriptor as a graph with concepts in the nodes and corresponding relevance points. The descriptor is used to generate the summary in a form of tree. Finally, we highlight the most important keywords in the original text. Presented experiments demonstrate the proposed approach, which successfully summarizes and highlights meaningful medical information.

Original language	English
Title of host publication	19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Proceedings
Publisher	Association for Computing Machinery
Pages	58-65
Number of pages	8
Volume	Part F134476
ISBN (Electronic)	9781450352994
DOIs	https://doi.org/10.1145/3151759.3151784
Publication status	Published - 2017 Dec 4
Event	19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Salzburg, Austria Duration: 2017 Dec 4 → 2017 Dec 6

Other

Other	19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017
Country	Austria
City	Salzburg
Period	17/12/4 → 17/12/6

Keywords

Concept
Data mining
Document descriptor
Information retrieval
Medical documents processing
Ontology
Summary generation

ASJC Scopus subject areas

Human-Computer Interaction
Computer Networks and Communications
Computer Vision and Pattern Recognition
Software

Access to Document

10.1145/3151759.3151784

Fingerprint Dive into the research topics of 'Medical documents processing for summary generation and keywords highlighting based on natural language processing and ontology graph descriptor approach'. Together they form a unique fingerprint.

Cite this

Dudko, A., Endrjukaite, T., & Kiyoki, Y. (2017). Medical documents processing for summary generation and keywords highlighting based on natural language processing and ontology graph descriptor approach. In 19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Proceedings (Vol. Part F134476, pp. 58-65). Association for Computing Machinery. https://doi.org/10.1145/3151759.3151784

Medical documents processing for summary generation and keywords highlighting based on natural language processing and ontology graph descriptor approach. / Dudko, Alexander; Endrjukaite, Tatiana; Kiyoki, Yasushi.

19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Proceedings. Vol. Part F134476 Association for Computing Machinery, 2017. p. 58-65.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Dudko, A, Endrjukaite, T & Kiyoki, Y 2017, Medical documents processing for summary generation and keywords highlighting based on natural language processing and ontology graph descriptor approach. in 19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Proceedings. vol. Part F134476, Association for Computing Machinery, pp. 58-65, 19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017, Salzburg, Austria, 17/12/4. https://doi.org/10.1145/3151759.3151784

Dudko A, Endrjukaite T, Kiyoki Y. Medical documents processing for summary generation and keywords highlighting based on natural language processing and ontology graph descriptor approach. In 19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Proceedings. Vol. Part F134476. Association for Computing Machinery. 2017. p. 58-65 https://doi.org/10.1145/3151759.3151784

Dudko, Alexander ; Endrjukaite, Tatiana ; Kiyoki, Yasushi. / Medical documents processing for summary generation and keywords highlighting based on natural language processing and ontology graph descriptor approach. 19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Proceedings. Vol. Part F134476 Association for Computing Machinery, 2017. pp. 58-65

@inproceedings{cfd2ca29d52c4ed286b693a7a6e0622c,

title = "Medical documents processing for summary generation and keywords highlighting based on natural language processing and ontology graph descriptor approach",

abstract = "In this paper a new method of data retrieval from free text documents in medical domain is proposed. Presented approach gives the document summary and highlights important keywords in the text to support further analysis of multiple medical documents. The document is processed with natural language processing techniques to find medical keywords and assign them to concepts in the medical ontology. These concepts contribute to higher levels in the hierarchy and build the document descriptor as a graph with concepts in the nodes and corresponding relevance points. The descriptor is used to generate the summary in a form of tree. Finally, we highlight the most important keywords in the original text. Presented experiments demonstrate the proposed approach, which successfully summarizes and highlights meaningful medical information.",

keywords = "Concept, Data mining, Document descriptor, Information retrieval, Medical documents processing, Ontology, Summary generation",

author = "Alexander Dudko and Tatiana Endrjukaite and Yasushi Kiyoki",

year = "2017",

month = dec,

day = "4",

doi = "10.1145/3151759.3151784",

language = "English",

volume = "Part F134476",

pages = "58--65",

booktitle = "19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Proceedings",

publisher = "Association for Computing Machinery",

note = "19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 ; Conference date: 04-12-2017 Through 06-12-2017",

}

TY - GEN

T1 - Medical documents processing for summary generation and keywords highlighting based on natural language processing and ontology graph descriptor approach

AU - Dudko, Alexander

AU - Endrjukaite, Tatiana

AU - Kiyoki, Yasushi

PY - 2017/12/4

Y1 - 2017/12/4

N2 - In this paper a new method of data retrieval from free text documents in medical domain is proposed. Presented approach gives the document summary and highlights important keywords in the text to support further analysis of multiple medical documents. The document is processed with natural language processing techniques to find medical keywords and assign them to concepts in the medical ontology. These concepts contribute to higher levels in the hierarchy and build the document descriptor as a graph with concepts in the nodes and corresponding relevance points. The descriptor is used to generate the summary in a form of tree. Finally, we highlight the most important keywords in the original text. Presented experiments demonstrate the proposed approach, which successfully summarizes and highlights meaningful medical information.

AB - In this paper a new method of data retrieval from free text documents in medical domain is proposed. Presented approach gives the document summary and highlights important keywords in the text to support further analysis of multiple medical documents. The document is processed with natural language processing techniques to find medical keywords and assign them to concepts in the medical ontology. These concepts contribute to higher levels in the hierarchy and build the document descriptor as a graph with concepts in the nodes and corresponding relevance points. The descriptor is used to generate the summary in a form of tree. Finally, we highlight the most important keywords in the original text. Presented experiments demonstrate the proposed approach, which successfully summarizes and highlights meaningful medical information.

KW - Concept

KW - Data mining

KW - Document descriptor

KW - Information retrieval

KW - Medical documents processing

KW - Ontology

KW - Summary generation

UR - http://www.scopus.com/inward/record.url?scp=85044281145&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85044281145&partnerID=8YFLogxK

U2 - 10.1145/3151759.3151784

DO - 10.1145/3151759.3151784

M3 - Conference contribution

AN - SCOPUS:85044281145

VL - Part F134476

SP - 58

EP - 65

BT - 19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Proceedings

PB - Association for Computing Machinery

T2 - 19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017

Y2 - 4 December 2017 through 6 December 2017

ER -