Single-document summarization based on semantics

Abstract

Single-document summarization goals to create a compressed summary while retaining the theme of the original document.Many approaches use statistics and machine learning techniques to extract sentences from a document.Because single document has limited information,the main approaches are of no effect.Therefore,a new single-document summarization framework based on semantics was proposed.First,the sentence-sentence similarity was calculated.After that modified K-Medoids clustering algorithm was used to cluster the sentences.Finally,the most informative sentence was chosen from each cluster to form the summary.The experimental results demonstrate the improvement of the summary quality by using semantics information.