Extractive Persian Summarizer for News Websites

Abstract

Automatic extractive text summarization is the process of condensing textual information while preserving the important concepts. The proposed method after performing pre-processing on input Persian news articles generates a feature vector of salient sentences from a combination of statistical, semantic and heuristic methods and that are scored and concatenated accordingly. The scoring of the salient features is based on the article’s title, proper nouns, pronouns, sentence length, keywords, topic words, sentence position, English words, and quotations. Experimental results on measurements including recall, F-measure, ROUGE-N are presented and compared to other Persian summarizers and shown to provide higher performance.