VIKOR Algorithm Based on Cuckoo Search for Multi-document Text Summarization

Ali, Zuhair Hussein; Noor, Ameen A.; Jassim, Muntaha Abood

doi:10.1007/978-3-030-38752-5_5

Cited by 3 publications

(1 citation statement)

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“…Lamsiyah, Mahdaouy [ 15 ] described the steps of extractive summarization as three-fold: cleaning and representation of input text, scoring of sentences according to their importance, and the sentence selection step, which involves the selection of sentences with the highest scores to form a summary. Extractive summarization is further divided based on its purpose into query-based methods, for example, the methods proposed by Mangalampati and Ponnuru [ 16 ], Van Lierde and Chow [ 17 ]; domain-specific methods, such as the methods proposed by Cao, Luo [ 18 ], Gupta, Sharaff [ 19 ] or generic methods, such as the methods proposed by Alami, Mallahi [ 20 ], Alia, Noora [ 21 ]. Based on context, extractive ATS methods can be divided into indicative, such as the methods proposed by Narayan, Cohen [ 22 ] and informative, such as the methods proposed by Vollmer, Golab [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Graph-based extractive text summarization method for Hausa text

Bichi,

Samsudin,

Hassan

et al. 2023

PLoS ONE

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Automatic text summarization is one of the most promising solutions to the ever-growing challenges of textual data as it produces a shorter version of the original document with fewer bytes, but the same information as the original document. Despite the advancements in automatic text summarization research, research involving the development of automatic text summarization methods for documents written in Hausa, a Chadic language widely spoken in West Africa by approximately 150,000,000 people as either their first or second language, is still in early stages of development. This study proposes a novel graph-based extractive single-document summarization method for Hausa text by modifying the existing PageRank algorithm using the normalized common bigrams count between adjacent sentences as the initial vertex score. The proposed method is evaluated using a primarily collected Hausa summarization evaluation dataset comprising of 113 Hausa news articles on ROUGE evaluation toolkits. The proposed approach outperformed the standard methods using the same datasets. It outperformed the TextRank method by 2.1%, LexRank by 12.3%, centroid-based method by 19.5%, and BM25 method by 17.4%.

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