Automatic Persian Text Summarization Using Linguistic Features from Text Structure Analysis

With the remarkable growth of textual data sources in recent years, easy, fast, and accurate text processing has become a challenge with significant payoffs. Automatic text summarization is the process of compressing text documents into shorter summaries for easier review of its core contents, which must be done without losing important features and information. This paper introduces a new hybrid method for extractive text summarization with feature selection based on text structure. The major advantage of the proposed summarization method over previous systems is the modeling of text structure and relationship between entities in the input text, which improves the sentence feature selection process and leads to the generation of unambiguous, concise, consistent, and coherent summaries. The paper also presents the results of the evaluation of the proposed method based on precision and recall criteria. It is shown that the method produces summaries consisting of chains of sentences with the aforementioned characteristics from the original text.