Improving Readability through Individualized Summary Extraction, Using Interactive Genetic Algorithm

The impact of individualized summarization is high, because a summary would be difficult to understand by all if summarized in a generic manner. When sentences are important as well as readable to the learner with reading difficulties, the summary might be useful for better comprehension of the text. In this article, we propose an Interactive Genetic Algorithm-based individualized summarization to maximize the readability of selected important sentences. The individualized summarization applies to the educational domain using readability-based features to extract readable important sentences. Inclusion of features representing reading difficulty should not dilute the informative score of the summary, moreover it aids the learner who has reading difficulties to comprehend the complete text better, using the summary as supplementary to the complete text and not as a substitute for it. The experimental results derived from intrinsic evaluation shows that individual summary extraction performs better than a GA-based approach and a baseline approach. Moreover, user -based direct evaluation also supports individualized summarization for improving the readability by the target audience.