An Effective Approach for Abstractive Text Summarization using Semantic Graph Model

Keywords Text summarization, Multi-document abstractive summarization, Semantic graph model, Sentence embedding, Graph-based ranking algorithm (ProQuest: ... denotes formulae omited.) Introduction In this digitalized era, it is easy to share and extract information from the world wide web. In the re...

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Bibliographic Details
Published in:Annals of the Romanian society for cell biology Vol. 25; no. 4; pp. 13925 - 13933
Main Authors: Selvan, R Senthamizh, Arutchelvan, K
Format: Journal Article
Language:English
Published: Arad "Vasile Goldis" Western University Arad, Romania 01.01.2021
Subjects:
Algorithms
Archives & records
Artificial intelligence
Cosmology
Embedding
Information processing
Literature reviews
Methods
Natural language processing
Semantics
World Wide Web
ISSN:2067-3019, 2067-8282
Online Access:Get full text
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Summary:Keywords Text summarization, Multi-document abstractive summarization, Semantic graph model, Sentence embedding, Graph-based ranking algorithm (ProQuest: ... denotes formulae omited.) Introduction In this digitalized era, it is easy to share and extract information from the world wide web. In the research paper [26] the authors have introduced an availability model dependent on diagram, which expects that hubs which are connected to a few different hubs are most likely to convey critical data. In this proposed work it has introduced a new ranking algorithm using the degree of vertices to rank the connected sentences. Summary Generation A.Text Preprocessing In the natural language processing, preprocessing is the precise step that is used to clean and transform the data for the
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ISSN:2067-3019
2067-8282