Online Summarization of Microblog Data: An Aid in Handling Disaster Situations

Abstract

During any natural disaster or unfortunate accident, both civilians and responders need information on an urgent basis. In such events, microblogging sites particularly Twitter plays an important role in providing real-time information. The raw form of microblog tweets is prodigiously informative but massive in size. The end-users and data analysts have to go through millions of tweets before extraction of any information. To ease the process and extract only relevant information, artificial intelligence (AI)-based techniques can be incorporated to generate summaries from the incoming information. Moreover, tweets keep on arriving continuously in a streaming manner, and therefore in ideal cases, the summaries also need to be updated continuously. In this work, we have proposed a clustering-based summary generation approach that takes multiviewed representations of data and utilizes a new variant of generative adversarial network (GAN) named triple-GAN to perform clustering. Triple-GAN consists of three networks, a generator, a discriminator, and a separator. Maintaining equilibrium among these networks requires proper parameter tuning which makes training of GAN difficult. In the literature, GAN-based techniques have been extensively applied to image datasets. In the proposed method, we have explored the usage of GAN for text data in an unsupervised manner and the analysis of the training of GAN has also been reported. The developed method opens up a new direction in utilizing GAN for solving clustering problem of text data. The proposed method is applied to two versions of four disaster-based microblog datasets and obtained results are compared with many existing and a few baseline methods. The comparative study illustrates the superiority and efficacy of the developed method.