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Today, automated extractive text summarization is one of the most common techniques for organizing information. In extractive summarization, the most appropriate sentences are selected from the text and build a representative summary. Therefore, probing for the best sentences is a fundamental task. This paper has coped with extractive summarization as a multi-objective optimization problem and proposed a language-independent, semantic-aware approach that applies the harmony search algorithm to generate appropriate multi-document summaries. It learns the objective function from an extra set of reference summaries and then generates the best summaries according to the trained function. The system also performs some supplementary activities for better achievements. It expands the sentences by using an inventive approach that aims at tuning conceptual densities in the sentences towards important topics. Furthermore, we introduced an innovative clustering method for identifying important topics and reducing redundancies. A sentence placement policy based on the Hamiltonian shortest path was introduced for producing readable summaries. The experiments were conducted on DUC2002, DUC2006 and DUC2007 datasets. Experimental results showed that the proposed framework could assist the summarization process and yield better performance. Also, it was able to generally outperform other cited summarizer systems.

The present study aims at investigating the effect of using the native language as a pedagogic intervention on the complexity of Iranian EFL learners' English oral productions. A sample of 39 male and female adult English learners of B1 and B2 CEFR proficiency levels was recruited to participate in this study. They were placed into two intact classes (i.e., as already determined by the institution’s authorities) and each class was randomly chosen to serve as either the experimental (EG) or the control (CG) group. Improving the learners’ speaking ability was the focus of both groups while only the EG was asked to orally produce the equivalents of Persian sentences presented to them. In order to measure the lexical and grammatical complexity of oral productions of the learners, two parallel speaking tests of IELTS 10, in the form of two oral interviews, were used as pre- and post-test oral interviews. A MANCOVA test was run to compare the performance of the two groups in terms of their lexical and grammatical complexity after the treatment. Results indicated that the EG's lexical and grammatical complexity improved as compared to the CG, and the improvement in both of these variables was statistically significant.

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