# Automatic Short Text Summarization Techniques in Social Media Platforms
## 7 ASTS Techniques for Social Media Platforms
Their method efficiently extracts and summarizes tweets containing situational information. These studies contribute to the advancement of automatic short text summarization in the context of social media, particularly for disaster-related content. The outcomes of their research were as follows: the ROUGE-1 F-score is 0.5770 for the event NEquake and 0.6602 for HDerail. [PERSON] et al. introduced a classification-based method for summarizing microblogs during outbreaks [52]. They used Twitter data from two recent outbreaks (Ebola and MERS) to build an automatic classification approach useful for categorizing tweets into different disease-related categories. Their approach efficiently categorizes raw tweets using low-level lexical class-specific features to extract essential information. Their study yielded a ROUGE-1 recall of 0.4961 and an F-score of 0.4980 for Ebola and a ROUGE-1 recall of 0.3862 and an F-score of 0.3758 for MERS. [PERSON] et al. proposed an approach that generates high-quality summaries by considering the social context of user-generated content and third-party sources [64]. They utilize learning to rank (L2R), utilizing Ranking SVM, with features such as sentences, user-generated content, and third-party sources to score and select sentences for the summaries, which can be retrieved through a score-based or voting mechanism. The model achieved a ROUGE-1 of 0.230 on comments of the SoLSCSum dataset. [PERSON] et al. introduced a generic social event summarization approach for Twitter streams [65]. The method includes participant detection, sub-event detection, and tweet summary extraction. They used an online clustering method for participant detection and an online temporal content mixture model Expectation Maximization (EM) algorithm for sub-event detection, making the approach applicable to natural tweet streams. The best evaluation results were 0.3970 of the new evaluation metric ROUGET\(-\)1 F-1 score, a modified evaluation metric of ROUGE-N, on the play-by-play data (Celtics vs. Heat) from ESPN. In their study, [PERSON] and [PERSON] introduced a novel approach called Social Networks with Sparse Reconstruction (SNSR) to summarize Twitter data, especially in short and noisy situations [66]. To achieve this, they formulated tweet relationships as social regularization and integrated them into a group sparse optimization framework. The researchers created gold-standard Twitter summary datasets covering 12 different topics for evaluation. These datasets were built using publicly available Twitter data collected by the University of Illinois as the raw data. The experimental results on these datasets demonstrated the effectiveness of their approach, with a ROUGE-1 score of 0.44887, ROUGE-2 score of 0.13882, and ROUGE-SU4 score of 0.18147. The research conducted by [PERSON] and [PERSON] tackles the task of detecting situational tweets using various deep learning architectures, including Convolutional Neural Network (CNN), long short-term memory (LSTM), bi-directional long short-term memory (BLSTM), and bi-directional long short-term memory with attention (BLSTM attention) [67]. Notably, these deep learning models are applied to both English and Hindi language tweets to identify situational information during disasters. In countries like India, during disasters, some tweets are posted in Hindi, and their information may not be available in English. To address this multilingual scenario, experiments are carried out on diverse disaster datasets, such as the Hyderabad bomb blast, Hagupit cyclone, Nepal Earthquake, Harda rail accident, and Sandy Hook shooting, considering both in-domain and cross-domain situations. The best performance results obtained for the deep learning models were as follows: On the TypHagupit dataset, the model achieved 98.72% recall, 74.37% F1 score, and 65.98% accuracy. On the SanHShoot dataset, it attained 95.56% recall, 79.53% F1 score, and 75.41% accuracy.