{'fraction_non_alphanumeric': 0.02081165452653486, 'fraction_numerical': 0.0, 'mean_word_length': 5.0125, '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}}
Voting Models for Summary Extraction from Text Documents

Electronic information - web pages, text documents, etc. are rapidly expanding due to the exponential growth of the World Wide Web (WWW). Information which are available through online search often provide readers with large collection of texts. Although easy access to online information had made great impact to the people, on the other hand, it has also caused them problem in facing information overload. Providing a solution to digest various information sources is indeed necessary to treat such problem. Especially in the case concerning online text sources, one study which is being actively researched is the field of automatic text summarization. In this paper, we propose the use of voting models, an effective approach in ranking aggregates tasks, to treat text summarization. Here, we will discuss how voting models can be adapted to the task of sentence ranking to generate text summaries.