Automatic Text Summarization Using Deep Learning and Reinforcement Learning

Abstract

AbstractChoosing relevant information from a giant source of data available online is a difficult and challenging task. Automatic summarization can address this challenge. Summarization is the task of condensing a chunk of text to a shorter version, which reduces the size of the initial text and simultaneously preserves the meaning of content. This model proposes automatic text summarization based on the reinforcement learning method and uses a deep learning network to estimate Q value. Here, we use rouge to analyze the performance of our model. ROUGE is used for evaluating automatic text summarization. The three phases of the project are text processing, text formation, and text evaluation. In text processing, select a set of sentences using latent semantic analysis and form a summary using reinforcement and deep Q network. And then, the summary is evaluated using rouge. KeywordsReinforcement learningDeep learning networkLatent semantic analysisRouge