  The huge amount of information shared in Twitter during disaster events are
utilized by government agencies and humanitarian organizations to ensure quick
crisis response and provide situational updates. However, the huge number of
tweets posted makes manual identification of the relevant tweets impossible. To
address the information overload, there is a need to automatically generate
summary of all the tweets which can highlight the important aspects of the
disaster. In this paper, we propose an entropy and diversity based summarizer,
termed as EnDSUM, specifically for disaster tweet summarization. Our
comprehensive analysis on 6 datasets indicates the effectiveness of EnDSUM and
additionally, highlights the scope of improvement of EnDSUM.
