# WATS-SMS: A T5-Based French Wikipedia Abstractive Text Summarizer for SMS
## 1 Introduction  
One of the most fascinating advances in the field of artificial intelligence is the ability of computers to understand natural language. Natural language processing (NLP) is an active field of research encompassing text and speech processing. With the huge amount of textual content available on the Internet, the search for relevant information is becoming difficult. Therefore, one tremendous application of NLP remains the text summarization. According to [PERSON] [1], text summarization aims to distil the most important information from one or several sources to generate an abridged version of the original document(s) for a particular user or task. Text summarization is usually composed of three steps: pre-processing, processing and post-processing [2]. The first step prepares the text to summarize by performing some tasks such as sentence segmentation, word tokenization or stop-words removal. The second step really performs the summarization of the text using a summarization technique based on one of the three approaches: extractive, abstractive and hybrid. Finally, the post-processing aims to address some last issues such as anaphora resolution before generating the final summary [3, 4, 5]. Text summarization is applied for several purposes including news summarization [6; 7], opinion or sentiment summarization [8; 9], tweet summarization [10], email summarization [11], scientific papers summarization [12; 13] and even web page summarization [14]. The latter offers the potential to provide an overview of web pages to devices with limited features. However, although the rate of use of mobile devices is increasing in remote and/or rural areas of developing countries, the bulk of those devices offers limited features with no browser and limited connectivity options [15]. The choice of basic phones with limited features is justified by three main reasons. First, it is the cost. Even if the price of smartphone is decreasing, it is still difficult for people living in rural areas with less than USD 1 per day to afford a smartphone [16]. Second is the lack of energy and the need of long-lasting battery. A full battery recharge of a smartphone usually lasts a day, compared to basic phones that can last several days. The third and not the least is the network coverage. Due to the lack of guarantee of return on investment for Telco operators, most part of the rural regions are unconnected and struggle with simple GSM network in the best case. This is justified by the low Internet penetration rate in developing countries, mainly in sub-Saharan Africa, as shown by the Internet World Stats (IWS) ([[https://www.internetworldstats.com/stats1.htm](https://www.internetworldstats.com/stats1.htm)]([https://www.internetworldstats.com/stats1.htm](https://www.internetworldstats.com/stats1.htm)), accessed on 10 June 2021). According to IWS, the average penetration rate of Internet in Africa in the fourth quarter of 2020 is only 43% compared to 64.2% as the world average. Despite efforts that have been made to provide Wi-Fi-based wireless connectivity in certain locations [17; 18], a large part of those devices is working only with the GSM network. Therefore, the only mean to provide relevant information to those users is through SMS. An important sector in rural regions, especially in sub-Saharan Africa, remains education that experiences teacher shortage [19; 20]. Providing relevant content to sustain the sector has become a priority. Among freely available educational resources, the Wikipedia encyclopedia represents one of the largest and most recognizable reference resources [21]. It is therefore an excellent choice to mitigate teacher shortage by providing relevant information to students. However, due to character limitation when using SMS, a complete Wikipedia page cannot be always sent through SMS, at the risk of losing relevant information. Thus, the summarization of the page is mandatory to provide the quintessence of the page. Several works have shown potential to summarize Wikipedia pages (e.g., [22; 23]).