Grapharizer: A Graph-Based Technique for Extractive Multi-Document Summarization

Featured Application: A graph-based technique tested on a benchmark dataset and augmented by machine learning techniques to provide a concise, informative, and grammatically correct summary. In the age of big data, there is increasing growth of data on the Internet. It becomes frustrating for users to locate the desired data. Therefore, text summarization emerges as a solution to this problem. It summarizes and presents the users with the gist of the provided documents. However, summarizer systems face challenges, such as poor grammaticality, missing important information, and redundancy, particularly in multi-document summarization. This study involves the development of a graph-based extractive generic MDS technique, named Grapharizer (GRAPH-based summARIZER), focusing on resolving these challenges. Grapharizer addresses the grammaticality problems of the summary using lemmatization during pre-processing. Furthermore, synonym mapping, multi-word expression mapping, and anaphora and cataphora resolution, contribute positively to improving the grammaticality of the generated summary. Challenges, such as redundancy and proper coverage of all topics, are dealt with to achieve informativity and representativeness. Grapharizer is a novel approach which can also be used in combination with different machine learning models. The system was tested on DUC 2004 and Recent News Article datasets against various state-of-the-art techniques. Use of Grapharizer with machine learning increased accuracy by up to 23.05% compared with different baseline techniques on ROUGE scores. Expert evaluation of the proposed system indicated the accuracy to be more than 55%.