Generic and Update Multi-Document Text Summarization based on Genetic Algorithm

In this paper, we addressed the generic and update text summarization tasks of a set of documents as a combinatorial optimization problem through a genetic algorithm and unsupervised textual features. Particularly under the news domain, input documents are a set of articles of varying sizes covering the same event. The main advantage of the proposed method is that it is language-independent. The experimental results demonstrated that the method performs well for both kinds of summarization. Moreover, we calculated the heuristics for update text summarization like a benchmark to compare state-of-the-art methods.