Effectiveness of Fuzzy Graph Based Document Model

Graph-based document models have good capabilities to reveal inter-dependencies among unstructured text data. Natural language processing (NLP) systems that use such models as an intermediate representation have shown good performance. This paper proposes a novel fuzzy graph-based document model and...

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Bibliographic Details
Published in:KSII transactions on Internet and information systems 2024, 18(8), , pp.2178-2198
Main Authors: Aswathy, M.R, Raj, P.C. Reghu, Ramanujan, Ajeesh
Format: Article
Language:English
Subjects:
Algorithms
Computational linguistics
Fuzzy algorithms
Fuzzy logic
Fuzzy systems
Graph theory
Language processing
Natural language interfaces
컴퓨터학
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Summary:Graph-based document models have good capabilities to reveal inter-dependencies among unstructured text data. Natural language processing (NLP) systems that use such models as an intermediate representation have shown good performance. This paper proposes a novel fuzzy graph-based document model and to demonstrate its effectiveness by applying fuzzy logic tools for text summarization. The proposed system accepts a text document as input and identifies some of its sentence level features, namely sentence position, sentence length, numerical data, thematic word, proper noun, title feature, upper case feature, and sentence similarity. The fuzzy membership value of each feature is computed from the sentences. We also propose a novel algorithm to construct the fuzzy graph as an intermediate representation of the input document. The Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric is used to evaluate the model. The evaluation based on different quality metrics was also performed to verify the effectiveness of the model. The ANOVA test confirms the hypothesis that the proposed model improves the summarizer performance by 10% when compared with the state-of-the-art summarizers employing alternate intermediate representations for the input text. Keywords: Eigenvalue, Fuzzy graph, Membership function, Natural language processing, Summary generation, Text features.
ISSN:1976-7277
1976-7277
DOI:10.3837/tiis.2024.08.007