Clustered genetic semantic graph approach for multi-document abstractive summarization

Abstract

Multi-document summarization aims to produce a compressed version of numerous online text documents and preserves the salient information. A particular challenge for multi-document summarization is that there is an inevitable overlap in the information stored in different documents. Thus, effective summarization methods that merge similar information across the documents are desirable. This paper introduces a clustered genetic semantic graph approach for multi-document abstractive summarization. The semantic graph from the document set is constructed in such a way that the graph vertices represent the predicate argument structures (PASs), extracted automatically by employing semantic role labeling (SRL); and the edges of graph correspond to semantic similarity weight determined from PAS-to-PAS semantic similarity, and PAS-to-document relationship. The PAS-to-document relationship is expressed by different features, weighted and optimized by genetic algorithm. The salient graph nodes (PASs) are ranked based on modified weighted graph based ranking algorithm. The clustering algorithm is performed to eliminate redundancy in such a way that representative PAS with the highest salience score from each cluster is chosen, and fed to language generation to generate summary sentences. Experiment of this study is performed using DUC-2002, a standard corpus for text summarization. Experimental results indicate that the proposed approach outperforms other summarization systems.