Clustered genetic semantic graph approach for multi document abstractive summarization
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.
2016 International Conference on Intelligent Systems Engineering (ICISE)