Open AccessSingle Document Summarization Based on Grey Wolf Optimization
Author(s) -
Moein Salimi Sartakhti,
Ahmad Yoosofan,
Ali Asghar Fatehi,
Ali Rahimi
Publication year - 2020
Publication title -
global journal of computer sciences
Language(s) - English
Resource type - Journals
ISSN - 2301-2587
DOI - 10.18844/gjcs.v10i2.5807
Subject(s) - automatic summarization , computer science , sentence , artificial intelligence , natural language processing , information retrieval , word (group theory) , genetic algorithm , function (biology) , fitness function , persian , machine learning , linguistics , philosophy , evolutionary biology , biology
The amazing growth of online services has caused an information explosion issue. Text summarisation is condensing the text into a small version and preserving its overall concept. Text summarisation is an important way to extract significant information from documents and offer that information to the user in an abbreviated form while preserving its major content. For human beings, it is very difficult to summarise large documents. To do this, this paper uses some sentence features and word features. These features assign scores to all the sentences. In this paper, we combine these features by Grey Wolf Optimiser (GWO). Optimisation of features gives better results than using individual features. This is the first attempt to show the performance of GWO for Persian text summarisation. The proposed method is compared with the genetic algorithm and the evolutionary strategy. The results show that our model will be useful in this research area. Keywords: Text summarisation, genetic algorithm, sentence, score function, evolutionary strategy.