  The huge popularity of social media platforms like Twitter attracts a large
fraction of users to share real-time information and short situational messages
during disasters. A summary of these tweets is required by the government
organizations, agencies, and volunteers for efficient and quick disaster
response. However, the huge influx of tweets makes it difficult to manually get
a precise overview of ongoing events. To handle this challenge, several tweet
summarization approaches have been proposed. In most of the existing
literature, tweet summarization is broken into a two-step process where in the
first step, it categorizes tweets, and in the second step, it chooses
representative tweets from each category. There are both supervised as well as
unsupervised approaches found in literature to solve the problem of first step.
Supervised approaches requires huge amount of labelled data which incurs cost
as well as time. On the other hand, unsupervised approaches could not clusters
tweet properly due to the overlapping keywords, vocabulary size, lack of
understanding of semantic meaning etc. While, for the second step of
summarization, existing approaches applied different ranking methods where
those ranking methods are very generic which fail to compute proper importance
of a tweet respect to a disaster. Both the problems can be handled far better
with proper domain knowledge. In this paper, we exploited already existing
domain knowledge by the means of ontology in both the steps and proposed a
novel disaster summarization method OntoDSumm. We evaluate this proposed method
with 4 state-of-the-art methods using 10 disaster datasets. Evaluation results
reveal that OntoDSumm outperforms existing methods by approximately 2-66% in
terms of ROUGE-1 F1 score.
