Abstract—This work proposes an approach to address the problem of improving content selection in automatic text summarization by using some statistical tools. This approach is a trainable summarizer, which takes into account several features, for each sentence to generate summaries. First, we investigate the effect of each sentence feature on the summarization task. Then we use all features in combination to train cellular automata (CA), genetic programming approach and fuzzy approach in order to construct a text summarizer for each model. Furthermore, we use trained models to test summarization performance. The proposed approach performance is measured at several compression rates on a data corpus composed of 17 English scientific articles. This article shows that some features are more important to construct models rather than other.
Index Terms—Fuzzy, genetic programming, cellular automata, machine learning.
Authors are with Islamic Azad University-Shahrekord Branch, Shahrekord from Iran (email: Khosravyan@iaushk.ac.ir; Kumarciemail@example.com).
Cite: Pouya Khosravian Dehkordi and Farshad Kiyoumarsi, "Using Cellular Automata to Construct Sentence Ranking," International Journal of Information and Education Technology vol. 1, no. 5, pp. 398-403, 2011.