### Related Work  
An important parameter related to the functionality of the platform includes the analysis of EO data and, specifically, EO images. A variety of such platforms exist [4; 5], most of which offer tools for the processing and analysis of data from satellites. The platform will offer the feature for automatic processing of EO images in real time. Global Disaster Alert and Coordination System (GDACS) [6] provides a disaster manager and disaster information systems worldwide and aims to fill information and coordination gaps in the aftermath of major disasters in the first phase. The platform is a cooperative framework of the United Nations and the European Commission. The GDACS provides real-time access to web-based disaster information systems and related coordination tools. Emergency and Disaster Information Service (EDIS) [7] is another platform that aims to monitor and document all the events on Earth that may cause disaster or emergency. The platform is operated by the National Association of Radio Distress-Signaling and Infocommunications, and it monitors and processes several foreign organizations' data to get quick and certified information. GLIDE [8] is another platform that provides extensive access to disaster information in one place, as well as a globally common unique ID code for disasters. Documents and data pertaining to specific events can be retrieved from various sources or are linked together using the unique GLIDE numbers. In addition to a list of disaster events, GLIDE can also generate summaries of disasters, e.g., by type, country, or year and also provides export of data sets in charts, tabular reports, or statistics. Different methods for the processing of EO images were used, according to literature, and particular methods were applied for building footprint extraction from EO images. For this reason, architectures for image segmentation [9; 10; 11] have been reviewed. Othermethods of processing of EO images used for the implementation of the platform concern the detection of the affected areas from floods and water in general [12; 13] and fire [14; 15]. One of the important features supported by the platform concerns the personalization of the generated text. For transferring users' writing style according to literature, methods to extract the user's sentiment [16; 17] have been implemented. In literature, the task of text generation includes a vast number of methods. A summary of multiple texts that maintains the information of these texts can be used for text synthesis. State-of-the-art approaches in the field of summarization, in general, and multi-document summarization, in particular, are all based on the Transformer [18], a model that leverages the mechanism of attention and achieves avoidance of utilizing re-currency and convolution [19; 20; 21; 22]. The state-of-the-art PEGASUS model [23] is a large Transformer-based encoder-decoder model pre-trained on massive text corpora with a self-supervised objective and is designed for use in both single and multi-document summarization cases. Notably, some other, more general purpose state-of-the-art NLP models, such as BART [24] and T5 [25], can produce results comparable with those of PEGASUS, in a few-shot and zero-shot multi-document summarization settings, suggesting that unlike single-document summarization, highly abstractive multi-document summarization remains a challenge [23]. Data-to-text generation (D2T) is a subtask for the generation of text using piles of data as input. Data-to-text generation is a promising subtask of study with many applications in media, paper generation, storytelling, etc. In their work, [PERSON], [PERSON] et al. [26] present the DATATUNER, a neural end-to-end data-to-text generation system that makes minimal assumptions about the data representation and target domain. DATATUNER achieves state-of-the-art results in various datasets. Another work presented by [PERSON], [PERSON] et al.