Automatic multi-documents text summarization using Binary Biology Migration Algorithm
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Date
2024
Authors
Mohamed BOUSSALEM
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Abstract
As the World Wide Web continues to expand, the process of identifying pertinent
information within its vast volume of documents becomes increasingly challenging. This
complexity necessitates the development of efficient solutions, one of which is automatic text
summarization; an active research area dedicated to extracting key information from extensive
text. The difficulties are further compounded when addressing multi-document text
summarization, due to the diversity of topics and sheer volume of information. In response to
this issue, this study introduces a novel approach based on swarm intelligence algorithm
called biology migration algorithm (BMA). Our proposed approach is; Binary Biology
Migration Algorithm for Multi-Document Summarization (BBMA-MDS). Viewing multidocument
summarization as a combinatorial optimization problem, this approach leverages
the biology migration algorithm to select an optimal combination of sentences. Evaluations of
the proposed algorithm's performance are conducted using the ROUGE metrics, which
facilitate a comparison between the automatically generated summary and the reference
summary, commonly known as the 'gold standard summary'. For a comprehensive evaluation,
the well-established DUC2002 and DUC2004 datasets are employed. The results demonstrate
the superior performance of the BBMA-MDS approach when compared to alternative
algorithms, including firefly and particle swarm optimization, as indicated by the selected
metrics. This study thus contributes effectively according to the evaluation to the field by
proposing BBMA-MDS as an effective solution for the multi-document text summarization
problem.