Fusion of self-organizing map and granular self-organizing map for microblog summarization

In this paper, we have proposed a fusion of two architectures, self-organizing map and granular self-organizing map (SOM + GSOM), for solving the microblog summarization task where a set of relevant tweets are extracted from the available set of tweets. SOM is used to reduce the available set of tweets to a smaller subset, and GSOM is used for extracting relevant tweets. The fusion of SOM + SOM is also accomplished to illustrate the effectiveness of GSOM over SOM in the second architecture. Moreover, only SOM version is also utilized to illustrate the potentiality of fusion in our proposed approaches. As similarity/dissimilarity measures play major role in any summarization system; therefore, to measure the same between tweets, various measures like word mover distance, cosine distance and Euclidean distance are also explored. The results obtained are evaluated on four datasets related to disaster events using ROUGE measures. Experimental results demonstrate that our best-proposed approach (SOM + GSOM) has obtained 17% and 5.9% improvements in terms of ROUGE-2 and ROUGE-L scores, respectively, over the existing techniques. The results are also validated using statistical significancet-test.