# Automatic Short Text Summarization Techniques in Social Media Platforms
## 7 ASTS Techniques for Social Media Platforms
During each update, essential new tweetsare initially extracted using the extractive module. The extraction process is then refined by explicitly detecting inconsistencies between the new tweets and previous summaries. The extractive module also captures sentence-level attention, which is later utilized by the abstractive module for acquiring word-level attention. This word-level attention is instrumental in rephrasing words to form a coherent summary. To validate their approach, The researchers carried out extensive experiments using real-world datasets. To minimize the labor involved in generating sufficient training data, they introduced automatic labeling steps, which were empirically validated for their effectiveness. Through these experiments, the IAEA framework achieved the following average ROUGE and BLEU scores: approximately 37 for ROUGE-1, 13 for ROUGE-2, 33 for ROUGE-L, 24 for BLEU-1, and 6 for BLEU-2. Additionally, the framework demonstrated an accuracy of 0.880, precision of 0.713, recall of 0.710, F1 score of 0.705, and AUC of 0.814. These results showcase the effectiveness and performance of the IAEA approach in real-time event summarization. In their research, [PERSON] et al. proposed a novel summarization method that revolves around sentiment topical aspect analysis, allowing for the automatic generation of comprehensive textual summaries for Twitter topics [47]. The method involves exploring the inherent sentiments associated with microblog posts and analyzing the topical aspects to enhance the summarization performance. The summarization process consists of three key stages. Initially, they adopted an approach to semantically enrich the microblog posts, extracting the sentiment and topic information linked to each post. Subsequently, for each topic-sentiment cluster of microblog posts, they constructed a Word Graph to identify different topical aspects within each specific cluster. Finally, they generated a comprehensive summary for each topic by applying various state-of-the-art summarization algorithms on each topical aspect of the Word Graph. To evaluate the effectiveness of their proposed method, the researchers performed a sequence of experiments using a Twitter dataset [48]. They evaluated the performance of their approach in comparison to existing document clustering algorithms. The results of the experiments revealed that Agglomerative clustering with Hybrid TF-IDF exhibited the most significant improvement (F-Measure delta: +0.255), followed by Bisect K-Means++ with Hybrid TF-IDF (F-Measure delta: +0.251), and then Hybrid TF-IDF alone (F-Measure delta: +0.249). The research conducted by [PERSON] et al. was centered on analyzing Twitter data related to cancer [49]. They collected data continuously over the course of one year and recorded observations comparing two sets of data. The larger dataset, however, contained more noise, which affected the performance of feature selection methods. To address this, the researchers introduced a pre-processing step and experimented with different combinations of feature selection methods used earlier. The experiments involved applying the K-means clustering technique first without feature selection and then with feature selection methods, namely variance threshold (FSVT) and Laplacian score (FSLS), along with two feature extraction methods--PCA and SVD. Both the old Twitter data (Cancer_tweets_2017) and the new, larger data (Cancer_tweets_2018) were used for experimentation. The intersection combination, utilizing the smallest feature set, exhibited better performance compared to other techniques. Additionally, SVD performed well for both datasets and various cluster sizes, requiring only five features. The researchers also explored a soft clustering algorithm, fuzzy C-means, and observed that the summary generated from the larger dataset was more meaningful than that from the smaller data, which yielded higher measures in previous experiments. This emphasized the importance of the additional pre-processing carried out.