An Additive FAHP Based Sentence Score Function for Text Summarization

Abstract

This study proposes a novel additive Fuzzy Analytical Hierarchy Process (FAHP) based sentence score function for Automatic Text Summarization (ATS), which is a method to handle growing amounts of textual data. ATS aims to reduce the size of a text while covering the important points in the text. For this aim, this study uses some sentence features, combines these features by an additive score function using some specific weights and produces a sentence score function. The weights of the features are determined by FAHP - specifically Fuzzy Extend Analysis (FEA), which allows the human involvement in the process, uses pair-wise comparisons, addresses uncertainty and allows a hierarchy composed of main features and sub-features. The sentences are ranked according to their score function values and the highest scored sentences are extracted to create summary documents. Performance evaluation is based on the sentence coverage among the summaries generated by human and the proposed method. In order to see the performance of the proposed system, two different Turkish datasets are used and as a performance measure, the F-measure is used. The proposed method is compared with a heuristic algorithm, namely Genetic Algorithm (GA). Resulting performance improvements show that the proposed model will be useful for both researchers and practitioners working in this research area.