CatSumm: Extractive Text Summarization based on Spectral Graph Partitioning and Node Centrality
Abstract
In this paper, we introduce CatSumm (Cengiz, Ali, Taner Summarization), a novel method for multi-document
document summarisation. The suggested method forms a summarization according to three main steps:
Representation of input texts, the main stages of the CatSumm model, and sentence scoring. A Text Processing
software, is introduced and used to protect the semantic loyalty between word groups at stage of representation of
input texts. Spectral Sentence Clustering (SSC), one of the main stages of the CatSumm model, is the
summarization process obtained from the proportional values of the sub graphs obtained after spectral graph
segmentation. Obtaining super edges is another of the main stages of the method, with the assumption that
sentences with weak values below a threshold value calculated by the standard deviation (SD) cannot be included
in the summary. Using the different node centrality methods of the CatSumm approach, it forms the sentence rating
phase of the recommended summarising approach, determining the significant nodes and hence significant nodes.
Finally, the result of the CatSumm method for the purpose of text summarisation within the in the research was
measured ROUGE metrics on the Document Understanding Conference (DUC-2004, DUC-2002) datasets. The
presented model produced 44.073%, 53.657%, and 56.513% summary success scores for abstracts of 100, 200 and
400 words, respectively.
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