def summarize_text(self, sites, articles): if (len(articles) < 1): return ["Not enought information about player"] summary_methods = NaiveBayesClassifier() return summary_methods.get_summary(sites, articles)
def NBCTest(): nbc = NaiveBayesClassifier() sites = [ "https://www.theplayerstribune.com/doublelift-league-of-legends-everyone-else-is-trash/" ] article_extractor = articles.ArticleExtractor('doublelift', 'league of legends', 5) #sites = article_extractor.get_websites() article = [article_extractor.parse_websites(site) for site in sites] string_text = list_to_string(article) string_text = string_text.replace("', '", ' ') string_text = string_text.replace('", "', ' ') summary = nbc.get_summary(string_text, 5) print("Summary:") for sentence in summary: print(u'\u2022 ' + sentence.lstrip("[]1234567890',.\" "))