Use of Genetic Algorithm for Cohesive Summary Extraction to Assist Reading Difficulties

Abstract EN

Learners with reading difficulties normally face significant challenges in understanding the text-based learning materials.

In this regard, there is a need for an assistive summary to help such learners to approach the learning documents with minimal difficulty.

An important issue in extractive summarization is to extract cohesive summary from the text.

Existing summarization approaches focus mostly on informative sentences rather than cohesive sentences.

We considered several existing features, including sentence location, cardinality, title similarity, and keywords to extract important sentences.

Moreover, learner-dependent readability-related features such as average sentence length, percentage of trigger words, percentage of polysyllabic words, and percentage of noun entity occurrences are considered for the summarization purpose.

The objective of this work is to extract the optimal combination of sentences that increase readability through sentence cohesion using genetic algorithm.

The results show that the summary extraction using our proposed approach performs better in F-measure, readability, and cohesion than the baseline approach (lead) and the corpus-based approach.

The task-based evaluation shows the effect of summary assistive reading in enhancing readability on reading difficulties.