Mining Graph-based Features in Multi-objective Framework for Microblog Summarization

Nowadays, micro-blogging sites are getting popular due to the involvement of a large number of users. In the case of natural disasters, a significant amount of relevant information (giving crucial information) are present amongst the tweets. Therefore, there is a need to develop a system that summarizes relevant tweets by extracting informative tweets. In the current paper, we have proposed an unsupervised approach for summarizing the relevant tweets namely, MOOTweetSumm+, which automatically selects the informative tweets. Several tweet-scoring measures: (a) anti-redundancy measuring the dissimilarity between tweets; (b) similarity with outputs provided by LexRank (a graph-based method measuring tweet importance based on the concept of eigen-vector centrality in a graph); (c) BM25 based ranking function; (d) tf-idf based ranking function; (e) length of the tweet; (f) re-tweet count, are simultaneously optimized utilizing a binary differential evolution algorithm. Further, two different versions of the LexRank, utilizing syntactic and semantic similarity, have also been explored. For evaluation, four different disaster-event related datasets are used, and performance is measured in terms of ROUGE scores. An ablation study is also performed to determine which set of measures is best suited for different datasets. From the results obtained, it is clearly evident that our approach improves by 13.2% and 5.8% in terms of ROUGE-2 and ROUGE-L scores, over the existing approaches, respectively.