Human Aided Text Summarizer "SAAR" Using Reinforcement Learning

Due to information revolution, huge amount of data is available over internet but retrieving correct and relevant data is not an easy task. The information retrieval from search engines is still far greater than that a user can handle and manage. Thus there is need of presenting the information in an abstract way so that one can easily infer the meaning without reading the whole document. In this paper, Human aided text summarizer "SAAR" is being proposed for single document. From the document, a term-sentence matrix is generated. The entries in the matrix are weight from Reinforcement Learning. Thus generated summary is shown to the user and if the user approve it then it is the final summary, otherwise new summary is generated as per the user feedback in form of keywords. Results of experiments on DUC2006 documents indicate that the performance of the proposed approach compares very favorably with other approaches in terms of precision, recall, and F-score.