Fuzzy swarm diversity hybrid model for text summarization
High quality summary is the target and challenge for any automatic text summarization. In this paper, we introduce a different hybrid model for automatic text summarization problem. We exploit strengths of different techniques in building our model: we use diversity-based method to filter similar sentences and select the most diverse ones, differentiate between the more important and less important features using the swarm-based method and use fuzzy logic to make the risks, uncertainty, ambiguity and imprecise values of the text features weights flexibly tolerated. The diversity-based method focuses to reduce redundancy problems and the other two techniques concentrate on the scoring mechanism of the sentences. We presented the proposed model in two forms. In the first form of the model, diversity measures dominate the behavior of the model. In the second form, the diversity constraint is no longer imposed on the model behavior. That means the diversity-based method works same as fuzzy swarm-based method. The results showed that the proposed model in the second form performs better than the first form, the swarm model, the fuzzy swarm method and the benchmark methods. Over results show that combination of diversity measures, swarm techniques and fuzzy logic can generate good summary containing the most important parts in the document.
diversity summarization feature fuzzy logic particle swarm optimization