# Automatic Short Text Summarization Techniques in Social Media Platforms
## 7 ASTS Techniques for Social Media Platforms
They evaluated their proposed approach using a dataset of text posts obtained from Instagram through popular hashtags using the developed crawler. The study analyzed 820 popular text posts from Instagram, revealing an accuracy rate of 80% and a precision rate of 75% for the proposed system. The results of their automatic text summarization system showed the following values: accuracy (75%), completeness (82%), precision (80%), harmonic mean (81%), and error rates (25%). In their work, [PERSON] et al. introduced a hybrid deep learning model named DEA-RNN, designed specifically to detect contextual bullying on the Twitter social media network [37]. They conducted a comprehensive evaluation using a dataset of 10,000 tweets and compared the performance of DEA-RNN to Bi-directional long short-term memory (Bi-LSTM), RNN, SVM, Multinomial Naive Bayes (MNB), and Random Forests (RF). DEA-RNN achieved an average accuracy of 90.45%, precision of 89.52%, recall of 88.98%, F1 score of 89.25%, and specificity of 90.94%. In [PERSON] et al. 's study, the researchers introduced OntoRealSumm, a novel approach capable of generating real-time tweet summaries for disasters with minimal human intervention [38]. OntoRealSumm tackles multiple challenges through a three-phase process. Experimental analysis revealed that OntoRealSumm outperforms existing research works, resulting in a 6% to 42% increase in ROUGE-N F1 scores. Furthermore, when evaluated on a dataset centered around the Harda Twin Train Derailment in India, where 31 people lost their lives and 100 were injured, OntoRealSumm demonstrated the best performance. It achieved F1 scores of 0.58, 0.29, and 0.31 for ROUGE-1, ROUGE-2, and ROUGE-L, respectively. Ref. [39] proposed a multi-task framework (MTLTS) to obtain trustworthy summaries. They treat tweet summarization as a supervised task, enabling the automatic learning of summary-worthy features and better generalization across domains. MTLTS achieves a ROUGE1-F1 of 0.501 on the PHEME dataset. [PERSON] et al. recently introduced a weekly supervised learning technique for coherent topic extraction and emotional reactions related to an event [40]. Their method provides representative microblogs and emotion distributions over time, summarizing the event effectively. The method obtains approximately a ROUGE-L of 0.4241, ROUGE-1 of 0.4787, ROUGE-2 of 0.2391, and ROUGE-3 of 0.1652 on the Mexico Earthquake dataset. The study of [PERSON] and [PERSON] delved into two extractive techniques aimed at summarizing events on Twitter [41]. The first approach capitalizes on the semantic characteristics of event-related terms, scoring tweets based on computed semantic values. The second method employs a graph convolutional network constructed from a tweet relation graph, enabling the generation of hidden features to estimate tweet salience. Consequently, the most salient tweets are chosen to form the event summary. During their experiments, the researchers obtained results with ROUGE and BLEU values of 0.647 and 0.490, respectively, using a dataset of 300 evaluation events. In the study by [PERSON] et al., the authors introduced three versions of an innovative approach for microblog summarization, named MOOTS1, MOOTS2, and MOOTS3 [42]. These versions were developed using the principles of multi-objective optimization. The underlying optimization strategy employed was the multi-objective binary differential evolution (MOBDE). The optimization process incorporated two measures to assess tweet similarity/dissimilarity: word mover distance and BM25. To assess the performance of the proposed approaches, the researchers evaluated them on four datasets focusing on disaster events, where only relevant tweets were included. The experimental results showcased the effectiveness of their best-proposed approach, MOOTS3, achieving a ROUGE-2 score of 0.3418 and a ROUGE-L score of 0.5009, respectively. In another study, [PERSON] et al.