{'fraction_non_alphanumeric': 0.02, 'fraction_numerical': 0.006666666666666667, 'mean_word_length': 5.01, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A graph based representative keywords extraction model from news articles

In an age of the deluge of information, a blizzard of documents such as news articles is being generated in a real-time. To grasp the contents of documents, keyword extraction methods have researched actively. In this paper, we propose a model to extract representative keywords of news articles based on graph model. We evaluate the accuracy of the proposed model compared with TextRank and TFIDF. The results show that proposed model's accuracy is improved to 40% and 90% respectively without increasing computational time.