Automatic Summarization for Academic Articles using Deep Learning and Reinforcement Learning with Viewpoints

Abstract

The purpose of this research is to develop a Viewpoint Refinement in Automatic Summarization (VPRAS) system for research articles. The system will reflect viewpoints of survey to support surveys stage for researchers and students. We collect academic articles using web scraping technology and construct training data by combining sections and sentences through analysis of the article's PDF structure. We use machine learning techniques to classify sentences in Japanese articles into viewpoints. In addition to supervised learning, we introduce reinforcement learning and Dynamic Programming (DP) to extract important sentences for each viewpoint. Finally, we implemented an agent to automatically extract summary sentences based on a reward function.