TMRSG: topic model based rich semantic graph method for abstractive multi-document summarization

Dr. K. ARUTCHELVAN; R. SENTHAMIZH SELVAN

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TMRSG: topic model based rich semantic graph method for abstractive multi-document summarization

Author(s)

Dr. K. ARUTCHELVAN Department of Computer and Information Science, Annamalai University, Chidambaram, Tamil Nadu, India.

R. SENTHAMIZH SELVAN Department of Computer and Information Science, Annamalai University, Chidambaram, Tamil Nadu, India.

Abstract

ive MDS using rich semantic graph-based methodology and topic modelling. The proposed approach generates summary by using graph-relations across multiple documents based on the relevant topics. The proposed approach is build using the centrality node ranking technique. The weighted graph ranking technique is applied to obtain the sequence of the sentences. The summary is generated using the highest rank scores of the sentences. The proposed technique is evaluated using the CNN/Daily Mail datasets.

Publication Details

Page(s) 4590-4597

DOI DOI not available

Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 12, Year: 2022

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Keywords

similarity measure, Topic Modelling, Semantic Graph, Multi-Document Abstractive Summarization, Sentence Ranking

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