# Automatic Short Text Summarization Techniques in Social Media Platforms
## 7 ASTS Techniques for Social Media Platforms
introduced two fusion models, namely the self-organizing map and granular self-organizing map (SOM+GSOM), designed to tackle the summarization challenges of social media and microblogs through clustering [43]. The models effectively retrieved the most relevant tweets from a collection, significantly reducing the number of tweets using SOM and further extracting the most pertinent tweets with GSOM. To assess their results, the researchers evaluated the models on four datasets related to disaster events: (a) the Sandy Hook elementary school shooting in the USA, (b) Uttarak-hand's floods, (c) Typhoon Hangupit in the Philippines, and (d) bomb blasts in Hyderabad. The SOM+GSOM architecture demonstrated impressive performance with ROUGE-L and ROUGE-2 scores of 0.5199 and 0.3661, respectively. In their work, [PERSON] et al. presented an unsupervised classification method based on a neural network to identify significant tweets based on their relevance [44]. The technique utilizes the self-organizing map (SOM) and the granular self-organizing map (GSOM) to reduce the number of tweets and group them into clusters. To determine the best GSOM parameter combinations, they employed an evolutionary optimization technique. The researchers assessed the effectiveness of their proposed approach on four microblog datasets related to disasters. The results obtained for their method were 0.3458 for ROUGE-2 and 0.5000 for ROUGE-L.  
[PERSON] et al. introduced an approach called Mythos, which serves to detect events and subevents within an event while generating abstract summaries and storylines to offer diverse perspectives on an event [45]. The Mythos system comprises three modules: an online incremental clustering algorithm is employed to identify small-scale events represented as small clusters, its event hierarchy generator generates larger events in the form of hierarchies, providing a broader context, and its summarization module uses a long short-term memory (LSTM)-based learning model to generate summaries at various levels, ranging from the most abstracted to the most detailed. These summaries at different levels are then used to create a comprehensive storyline for each event. To evaluate Mythos, the researchers conducted experimental analyses on various Twitter datasets from different domains, including the FA Cup final, Super Tuesday (ST) Primaries, US Elections (USEs), and SB. The comparison was made against existing approaches for event detection and summarization. The results of the evaluation showed that Mythos achieved the best F-measure score of 0.5644 on the USE dataset, highlighting its superior performance in event detection and summarization compared to other known approaches. [PERSON] and [PERSON] introduced a novel approach called summary-aware attention for abstractive summarization of social media short texts [26]. They employed source-to-summary attention to calculate the attended summary vectors. Furthermore, they utilized dual or fusion methods to generate summary-aware attention weights, taking into account both the source text and the generated summary. The evaluation of their model was conducted on the Large Scale Chinese Short Text Summarization (LCSTS) dataset, comprising more than 2 million real text-summary pairs gathered from the Chinese microblogging website Sina Weibo. The experimental results demonstrated that their model surpassed Seq2 Seq baselines. Moreover, the generated summaries effectively captured the essential ideas of the source text in a more coherent manner. The best performance achieved by their model was impressive, with a ROUGE-1 score of 40.3, a ROUGE-2 score of 27.8, and a ROUGE-L score of 38.4. In their paper, [PERSON] et al. introduced a real-time event summarization framework called IAEA (integrity-aware extractive-abstractive real-time event summarization) [46]. The core concept behind IAEA is the incorporation of an inconsistency detection module into a unified extractive-abstractive framework.