Clustering Technique for Policy Categorization and Text Summarization based on Deep Learning Approaches

Abstract

Every citizen has the right to control their own data in this digital society. For data privacy, policies are created by social media applications. The users often skip the entire policies of websites or applications to save energy and time without realizing the main points of these policies. Moreover, most of the hypermedia users won’t read the policies because of the verbose explanations and obscure language. If these privacy policies are summarized and classified into useful data, that will be beneficial for users. For that reason, text summarization models are used, which improve social media privacy by automatically summarizing very long privacy policies for users. Recently, many machine learning-based text summarization models have been introduced by researchers. However, these models suffer from issues like inaccuracies, scalability issues, and difficulty in tuning. To solve this problem, in this work, a hybrid classifier (transfer learning (TL)-based modified long short-term memory (LSTM) + bi-directional gated recurrent unit (Bi-GRU)) based policy categorization and text summarization model is proposed, starting with policy categorization as the initial step. During this phase, the data annotation process takes place using an Improved fuzzy C-means algorithm to provide an accurate policy categorization by dividing the policies into paragraphs. After clustering, a preprocessing phase is applied to each cluster, and features such as smoothed dispersion coefficient (SDC)-based term frequency-inverse document frequency (TF-IDF), Thematic features, and Bag of Words are extracted. These features are then used in the text summarization phase, where a hybrid classifier, combining TL-based modified LSTM and Bi-GRU models, is utilized. The fusion of TL-based modified LSTM and Bi-GRU produces a single model that generates the final summarized text. Analysis shows that the proposed hybrid classifier (TL-based modified LSTM + Bi-GRU) achieved superior accuracy of 95% and 95.6% on both APP-350 and BillSum datasets.