Visible to the public Extractive Persian Summarizer for News Websites

TitleExtractive Persian Summarizer for News Websites
Publication TypeConference Paper
Year of Publication2019
AuthorsKermani, Fatemeh Hojati, Ghanbari, Shirin
Conference Name2019 5th International Conference on Web Research (ICWR)
Date Publishedapr
Keywordsautomatic extractive text summarization, data mining, English words, extractive Persian summarizer, extractive text summarization, feature extraction, feature vector, genetic algorithms, heuristic methods, heuristical and semantical, Human Behavior, Libraries, natural language processing, Persian news articles, pre-processing, pubcrawl, Resiliency, salient features, salient sentences, Scalability, semantic methods, Semantics, sentence length, statistical, statistical methods, text analysis, textual information, Tools, Web sites
AbstractAutomatic 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.
Citation Keykermani_extractive_2019