IKDSumm: Incorporating key-phrases into BERT for extractive disaster tweet summarization

Online social media platforms, such as Twitter, are one of the most valuable sources of information during disaster events. Humanitarian organizations, government agencies, and volunteers rely on a concise compilation of such information for effective disaster management. Existing methods to make such compilations are mostly generic summarization approaches that do not exploit domain knowledge. In this paper, we propose a disaster-specific tweet summarization framework, IKDSumm, which initially identifies the crucial and important information from each tweet related to a disaster through key-phrases of that tweet. We identify these key-phrases by utilizing the domain knowledge (using existing ontology) of disasters without any human intervention. Further, we utilize these key-phrases to automatically generate a summary of the tweets. Therefore, given tweets related to a disaster, IKDSumm ensures fulfillment of the summarization key objectives, such as information coverage, relevance, and diversity in summary without any human intervention. We evaluate the performance of IKDSumm with 8 state-of-the-art techniques on 12 disaster datasets. The evaluation results show that IKDSumm outperforms existing techniques by approximately $2-79\text{\%}$ in terms of ROUGE-N F1-score.