Text Summary Augmentation for Intelligent Reading Assistant

This paper presents a technique to assist a reader. We aim to reduce manual efforts of the reader by leveraging the state-of-the-art document summarization techniques and providing summaries about unclear descriptions for each reader. Our system acts as a plug-and-play model that can be modified to support additional methodologies. As a backend of the system, we investigated several text summarization techniques and evaluated three techniques of them: TextRank, LexRank, and Luhn’s algorithm.