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',.\" "))