OntoDSumm : Ontology based Tweet Summarization for Disaster Events

TL;DR

This paper introduces OntoDSumm, an ontology-based tweet summarization method for disaster events that leverages domain knowledge to improve summarization accuracy, outperforming existing approaches significantly.

Contribution

The paper presents a novel ontology-based approach for disaster tweet summarization, effectively utilizing domain knowledge to enhance categorization and ranking steps.

Findings

Outperforms existing methods by 2-66% in ROUGE-1 F1 score

Effectively leverages domain ontology for better tweet clustering and ranking

Demonstrates robustness across 10 disaster datasets

Abstract

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.