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BRSA-ESDS: A Binary Reptile Search Algorithm for Extractive Single Document Summarization
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AI
DOI：10.24138/jcomss-2025-0187.png)
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Abstract
En 中文
summarization systems are becoming more popular due to the growing volume of text information in many real-life applications. This paper presents a novel approach to summarising a single document by modeling it as an optimization problem and using the Reptile Search Algorithm (RSA) to solve it. This algorithm is inspired by crocodile hunting behaviour, which includes two main steps encircling and hunting. The encircling step requires high walking or belly walking phases while the hunting step requires coordination or cooperation. In this study, we propose a binary version of this algorithm called BRSA-ESDS to implement an automatic text summarization system by choosing a subset of the sentences in the original text. This algorithm optimizes an objective function to preserve linguistic quality based on many factors, including readability and consistency in the compressed summary while improving its coverage. This model ensures the diversity and coverage of selected sentences in the summary by optimizing a harmonic average of the objective function factors. Additionally, this model controls the summary's length to ensure its readability. The results are compared with state-of-the-art approaches using ROUGE measures on the Document Understanding Conference (DUC) benchmark datasets. According to ROUGE scores, our approach consistently performs better than other methods.
Keywords:
Extractive text summarization
reptile search algorithm
multi-objective optimization
meta-heuristic algorithm
swarm intelligence
Journal
J
IF:
0.7
Papers:
38
Citations:
171
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