OntoDSumm: Ontology-Based Tweet Summarization for Disaster Events

Abstract

The huge popularity of social media platforms, such as 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 and unsupervised approaches found in the literature to solve the problem of first step. Supervised approaches require a huge amount of labeled data, which incurs cost as well as time. On the other hand, unsupervised approaches could not cluster tweet properly due to the overlapping keywords, vocabulary size, lack of understanding of semantic meaning, and so on, 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 with respect to a disaster. Both problems can be handled far better with proper domain knowledge. In this article, we exploited already existing domain knowledge by the means of ontology in both steps and proposed a novel disaster summarization method OntoDSumm. We evaluate this proposed method with six state-of-the-art methods using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$12$</tex-math> </inline-formula> disaster datasets. Evaluation results reveal that OntoDSumm outperforms the existing methods by approximately 2%–66% in terms of ROUGE-1 F1-score.