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

Abstract

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%.

Graph Stream Compression Scheme Based on Pattern Dictionary Using Provenance

“…During this process, various algorithms and techniques are applied to consider the relationships among vertices and edges, graph structure, and dynamic changes. Frequent pattern mining [1,[5][6][7]11,15,21] is a method of extracting meaningful information or patterns from data, used in various fields, such as pattern analysis, anomaly detection, and recommendation services. FP-Growth is a method that efficiently finds frequent patterns using an FP-tree structure [32,33].…”

“…One of the main challenges in a dynamic environment is that the size of the graph continuously increases over time. To efficiently manage infinitely increasing data within limited storage space, graph compression is essential, allowing for the efficient use of storage space to accommodate increasing amounts of data [17][18][19][20][21][22]. Techniques that incorporate graph pattern mining methods also exist [23][24][25][26][27][28].…”

GRAAL: Graph-Based Retrieval for Collecting Related Passages across Multiple Documents

“…Giarelis et al propose employing a "graph-of-docs" model to represent documents and their words to enhance text categorization [28] and feature selection [29]. Jalil et al [30] employ word graphs to improve text summarization.…”

Graph Stream Compression Scheme Based on Pattern Dictionary Using Provenance

“…During this process, various algorithms and techniques are applied to consider the relationships among vertices and edges, graph structure, and dynamic changes. Frequent pattern mining [1,[5][6][7]11,15,21] is a method of extracting meaningful information or patterns from data, used in various fields, such as pattern analysis, anomaly detection, and recommendation services. FP-Growth is a method that efficiently finds frequent patterns using an FP-tree structure [32].…”

“…One of the main challenges in a dynamic environment is that the size of the graph continuously increases over time. To efficiently manage infinitely increasing data within a limited storage space, graph compression is essential, allowing for the efficient use of storage space to accommodate increasing amounts of data [17][18][19][20][21][22]. Techniques that incorporate graph pattern mining methods also exist [23][24][25][26][27][28].…”