Go home now Header Background Image
Search
Submission Procedure
share: |
 
Follow us
 
 
 
 
Volume 21 / Issue 11

available in:   PDF (387 kB) PS (692 kB)
 
get:  
Similar Docs BibTeX   Write a comment
  
get:  
Links into Future
 
DOI:   10.3217/jucs-021-11-1454

 

PSO-Based Feature Selection for Arabic Text Summarization

Ahmed M. Al-Zahrani (King Saud University, Saudi Arabia)

Hassan Mathkour (King Saud University, Saudi Arabia)

Hassan Abdalla (King Saud University, Saudi Arabia)

Abstract: Feature-based approaches play an important role and are widely applied in extractive summarization. In this paper, we use particle swarm optimization (PSO) to evaluate the effectiveness of different state-of-the-art features used to summarize Arabic text. The PSO is trained on the Essex Arabic summaries corpus data to determine the best particle that represents the most appropriate simple/combination of eight informative/structure features used regularly by Arab summarizers. Based on the elected features and their relevant weights in each PSO iteration, the input text sentences are scored and ranked to extract the top ranking sentences in the form of an output summary. The output summary is then compared with a reference summary using the cosine similarity function as the fitness function. The experimental results illustrate that Arabs summarize texts simply, focusing on the first sentence of each paragraph.

Keywords: Arabic text summarization, Particle Swarm optimization, feature selection, natural language processing

Categories: B.4.4, D.3.3, H.3.1, H.3.6, I.2.6, I.5.4