Two-Level Text Summarization with Natural Language Processing

Abstract

Text summarization is the process of shortening a text document in order to create a summary covering important points, aspects of the original document. Text summarization methods are based on extractive model and abstractive model. Two-level text summarization is used to form summary of different news articles. In the first level, multiple news articles are read and first level summary is generated. These multiple summaries are then analyzed and a single summary concerning the news topic is generated in second-level. TextRank with TF-IDF algorithm is used which is an extractive summarization technique to create news summary. The performance of the summary is evaluated using ROUGE matrix.