ACO–RNN: A MULTI-OBJECTIVE ANT COLONY OPTIMIZATION AND RECURRENT NEURAL NETWORK FRAMEWORK FOR DISASTER TWEET SUMMARIZATION

The rapid growth of disaster-related content on social media offers valuable opportunities for situational awareness, yet it also creates major challenges due to noise, redundancy, and the rapidly evolving nature of crisis events. Existing disaster tweet summarization approaches, including ontology-based, embedding-based, and neural methods, typically address semantic relevance, redundancy reduction, or coherence in isolation. As a result, they struggle to simultaneously control redundancy, preserve temporal coherence, and remain scalable for real-time disaster response.This study is motivated by the need for a unified summarization framework that jointly addresses these challenges within a single, scalable, and unsupervised model. To this end, we propose Ant Colony Optimization and Recurrent Neural Network (ACO-RNN), a hybrid framework that integrates Word2Vec embeddings for semantic representation, Ant Colony Optimization (ACO) for redundancy-aware sentence selection, and a Recurrent Neural Network (RNN) to preserve sequential coherence across selected tweets. The summarization task is formulated as a multi-objective optimization problem that balances relevance, coverage, and diversity while minimizing redundancy. The proposed framework is evaluated on four benchmark disaster tweet datasets, namely TyHagupit, SandyHShoot, HydBlast, and UFlood, using Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. Experimental results show that ACO-RNN achieves ROUGE-1 F1-scores ranging from 0.587 to 0.614, with relative improvements of 8 to 13 percent over strong baseline methods, including OntoDSumm and IKDSumm, along with consistent gains in ROUGE-2 and ROUGE-L. These findings demonstrate that combining redundancy-aware optimization with sequential neural modeling effectively overcomes key limitations of existing approaches and produces concise, coherent, and informative summaries suitable for disaster response and situational awareness.