Mobile IoT device summarizer using P2P web search engine and inherent characteristic of contents

Abstract

Grasping the sense of web pages on small screen of mobile devices is too difficult, so mobile users prefer summarized reports. One popular technique to generate automatic summarized report is based on vector model. The vector model summarization methods are simple and low cost to implement on mobile devices. However, the quality of summarized reports of the model may not be good because there is a semantic difference between manual summarized reports and the machine summarized reports. In addition, the censored documents of the centralized search engine cannot accurately reflect the query of user in the summary results. To overcome this constraints, automatic mobile device summarizer using a semantic feature of pseudo relevance feedback and P2P web search engine is proposed. The proposed method increases the quality of summarized reports by using the latent features of documents and clustering technique. The method uses none censored documents of the P2P web search engine to more accurately reflect user requirements in summarized reports. The automatic summarizer consists of a server-side containerized auto-summary module for flexible management of the summarization functions and a user-side mobile device module to reduce the overload of the mobile device.