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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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

Ensieh Davoodijam¹, Nasser Ghadiri¹, Maryam Lotfi Shahreza²

¹Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran.

Journal of Biomedical Informatics

|February 21, 2021

Summary

This summary is machine-generated.

This study introduces MultiGBS, a novel multi-layer graph approach for automatic text summarization. It enhances information capture by processing word, semantic, and co-reference similarities simultaneously for better gist extraction.

Keywords:

Automatic text summarization Concept-based summarization Domain-specific summary Multi-graph text modeling Text mining

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Area of Science:

Natural Language Processing
Computational Linguistics
Information Retrieval

Background:

Automatic text summarization aims to provide concise overviews of documents.
Current methods often focus on single text features, risking information loss.
Domain-specific summarization requires advanced techniques to capture multifaceted information.

Purpose of the Study:

To propose a domain-specific text summarization method using a multi-layer graph model.
To integrate multiple textual features for more comprehensive sentence selection.
To improve the quality of automatic text summarization through a novel algorithm.

Main Methods:

Developed a multi-layer graph representation of documents.
Modeled word similarity, semantic similarity, and co-reference similarity as distinct layers.
Employed the MultiRank algorithm and concept identification for sentence selection.
Utilized Unified Medical Language System (UMLS) and tools like SemRep, MetaMap, and OGER for concept extraction.

Main Results:

The proposed MultiGBS algorithm demonstrated improved performance in text summarization.
Evaluation using ROUGE and BERTScore metrics indicated increased F-measure values.
Simultaneous processing of multiple features led to more informative summaries.

Conclusions:

The multi-layer graph approach effectively captures diverse textual features for summarization.
MultiGBS offers a superior method for domain-specific automatic text summarization.
This approach mitigates information loss common in single-feature summarization techniques.