IKDSumm: Incorporating Key-phrases into BERT for extractive Disaster
  Tweet Summarization
Piyush Kumar Garg Roshni Chakraborty Srishti Gupta Sourav Kumar Dandapat
Online social media platforms, such as Twitter, are one of the most valuable
sources of information during disaster events. Therefore, humanitarian
organizations, government agencies, and volunteers rely on a summary of this
information, i.e., tweets, for effective disaster management. Although there
are several existing supervised and unsupervised approaches for automated tweet
summary approaches, these approaches either require extensive labeled
information or do not incorporate specific domain knowledge of disasters.
Additionally, the most recent approaches to disaster summarization have
proposed BERT-based models to enhance the summary quality. However, for further
improved performance, we introduce the utilization of domain-specific knowledge
without any human efforts to understand the importance (salience) of a tweet
which further aids in summary creation and improves summary quality. 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% in terms of ROUGE-N F1-score.