# Automatic Short Text Summarization Techniques in Social Media Platforms
## 7 ASTS Techniques for Social Media Platforms
Moreover, the generated summaries from the Twitter dataset proved to be useful for the healthcare fraternity to understand the genuine feelings and opinions of patients or their relatives. This valuable insight can help improve the services and care provided to them, aligning with the holistic approach required in the healthcare industry. [PERSON] et al. made a twofold contribution to their paper. Firstly, they introduced the TES 2012-2016, a sizable evaluation collection specifically designed for Twitter event summarization [50]. This valuable collection, based on [PERSON]'s research [51], consists of 28 events, with an average of 3 million tweets per event. Secondly, the authors proposeda novel neural model named TSSuBERT for extractive tweet summarization. TSSuBERT incorporates several original features, including (i) the utilization of pre-trained language models and tweet context in the form of vocabulary frequencies and (ii) the automatic adjustment of output summary size, which makes it more practical for real-world applications compared to state-of-the-art models that always output summaries of fixed size, regardless of their usefulness. (iii) TSSuBERT is an incremental method, enabling efficiency gains, as it does not require re-evaluating all documents at each time increment. To evaluate their approach, they conducted experiments using two distinct Twitter collections: TES 2012-2016 and the collection introduced by [PERSON] et al. [52]. The results demonstrated that on the TES 2012-2016 dataset, TSSuBERT achieved a ROUGE-1 score of 0.111, a ROUGE-2 score of 0.018, and a COS Embed score of 0.741. On the collection from [PERSON] et al., the model attained a ROUGE-1 score of 0.412, a ROUGE-2 score of 0.192, and a COS Embed score of 0.935. In the paper by [PERSON] and [PERSON], they propose an unsupervised approximate graph clustering-based global event detection model for Twitter data [53]. The key contribution lies in their innovative graph-based Twitter data representation, which effectively incorporates uncertainty in tweet relationships while maintaining contextual and directional information. This approach ensures computational efficiency by keeping the runtime requirements low. To further enhance their model, they utilize component filtration of the Twitter graph, removing irrelevant tweet-pairs and edges. This focused approach allows for the selection of critical data points with a higher likelihood of representing an event. Additionally, they employ partial-k-based uncertain graph clustering to improve the model's runtime performance. The experimental performance analysis demonstrates the superiority of their proposed model compared to other competitors on different datasets. The precision, recall, and F1 score values obtained on various datasets are as follows:  
RePLab2013 Twitter data: precision score of 0.7098, recall score of 0.833, and F1 score of 0.766. Auspol Twitter data: precision score of 0.718, recall score of 0.695, and F1 score of 0.706. Common Diseases dataset: precision score of 0.714, recall score of 0.651, and F1 score of 0.681. Customer Support on Twitter dataset: precision score of 0.51, recall score of 0.651, and F1 score of 0.571. These results affirm the superior performance of their proposed model in event detection across various types of datasets. Automatic text summarization presents a multi-objective optimization (MOO) challenge, aiming to balance two conflicting objectives: retaining information from the source text while keeping the summary length concise. To address this problem, [PERSON] and [PERSON] devised a method that leverages an undirected graph to establish relationships between social media comments [54]. The approach employs the Multi-Objective Ant Colony Optimization (MOACO) algorithm to create summaries by selecting concise yet important comments from the graph, depending on the desired summary size.