An Iterative Graph-Based Generic Single and Multi Document Summarization Approach Using Semantic Role Labeling and Wikipedia Concepts

This paper proposes an innovative graph-based text summarization model for generic single and multi-document summarization. The approach involves four unique processing stages: parsing sentences semantically using Semantic Role Labeling (SRL), grouping semantic arguments while matching semantic roles to Wikipedia concepts, constructing a weighted semantic graph for each document and linking its sentences (nodes) through the semantic relatedness of the Wikipedia concepts. An iterative ranking algorithm is then applied to the document graphs to extract the most important sentences deemed as the summary. The empirical evaluation of the proposed summarization model on a standard dataset from the Document Understanding Conference (DUC) showed the effectiveness of the approach which outperformed the baseline comparators in terms of ROUGE scores.