| Autor: |
Boussalem, Mohamed, Aitouche, Samia, Hamouma, Moumen, Haouassi, Hichem, Rahab, Hichem, Bekhouche, Abdelaali |
| Předmět: |
|
| Zdroj: |
Revue d'Intelligence Artificielle; Oct2023, Vol. 37 Issue 5, p1147-1158, 12p |
| Abstrakt: |
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, the Binary Biology Migration Algorithm for Multi-Document Summarization (BBMA-MDS). Viewing multi-document 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 to the field by proposing BBMA-MDS as an effective solution for the multi-document text summarization problem [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
