{'fraction_non_alphanumeric': 0.01765650080256822, 'fraction_numerical': 0.0, 'mean_word_length': 5.303030303030303, '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}}
NLP-assisted exploration of texts

Retrieving information does not end with the identification of the top n relevant files. The identified text should then be presented to the user in a form that facilitates accessing the relevant information pieces. This work investigates the possibility of using lexical structures latent in the text to provide the reader with rich visual representations such as table-of-contents and topic index. The paper describes the approach for topic identification, reconstructing the hierarchical structure and the generation of sections' headings, as well as the back-end visualization system.