# Automatic Short Text Summarization Techniques in Social Media Platforms
## 7 ASTS Techniques for Social Media Platforms
These findings demonstrate the effectiveness of the proposed deep learning architectures for detecting situational tweets during disasters in both the English and Hindi languages. [PERSON] et al. utilized the distinctive attributes of pre-scheduled events in tweet streams to extract the key highlights and proposed a method for summarizing these highlights [68]. To assess the effectiveness of their algorithm, they conducted evaluations using tweets related to two episodes of the renowned TV show Game of Thrones, Season 7. The evaluation results for tweets centered on the highlights from Episode 3 were impressive, with scores of 38 for ROUGE-1, 10 for ROUGE-2, and 31.5 for ROUGE-L. In their work, [PERSON] et al. presented a tweet integration model based on identifying the shortest paths on a word graph [1]. To achieve this, they employed a topic modeling technique that automatically determines the number of topics within the tweets. Additionally, sentiment analysis was used to assess the percentage of opinions categorized as agreeing, disagreeing, or neutral for each issue. The outcome of this integration process is the creation of weighted directed graphs, where each graph corresponds to a topic and represents the shortest path on the word graph. To assess the model's performance, the researchers gathered 90,000 raw tweets from 20 Twitter users using the Streams API for Python. After pre-processing the tweets, they obtained a subset of 70,000 tweets, which was used for evaluation. The experimental results of the proposed method yielded the following scores: For ROUGE_N (F_measure: 0.30, precision: 0.31, and recall: 0.29), For ROUGE_L: (F_measure: 0.20, precision: 0.20, and recall: 0.19)  
Ref. [69] proposed an innovative model that utilizes an autoencoder as a supervisor for the sequence-to-sequence model, enhancing the internal representation for abstractive summarization. To further enhance the autoencoder's supervision, they introduced an adversarial learning approach. To evaluate the effectiveness of their model, they conducted experiments on the Large Scale Chinese Social Media Text Summarization Dataset (LCSTS). This dataset comprises more than 2,400,000 text-summary pairs extracted from the Sina Weibo website. The experimental results were highly promising, achieving the following ROUGE scores: (ROUGE-1: 39.2, ROUGE-2: 26.0, and ROUGE-L: 36.2). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref. ** & **Objective** & **Method** & **Domain** & **Dataset** & \begin{tabular}{c} **Evaluation** \\ **Metrics** \\ \end{tabular}  & **Observation** \\ \hline
[44] & Quality & \begin{tabular}{c} SOM, GSOM, and GA \\ \end{tabular}  & Disaster events & \begin{tabular}{c} Disaster events \\ UkFlood, Sandyhook, \\ Hblast, and Hagupit \\ \end{tabular}  & \ \begin{tabular}{c} ROUGE-2 = 0.3458 \\ ROUGE-2 = 0.5000 \\ \end{tabular}  & \begin{tabular}{c} Showed improvement in \\ ROUGE-2 and ROUGE-L score. \\ \end{tabular}  \\ \hline
[40] & Diversity & \begin{tabular}{c} Supervised \\ learning \\ \end{tabular}  & Event & \begin{tabular}{c} Collected tweets and \\ event-based dataset \\ \end{tabular}  & \ \begin{tabular}{c} ROUGE-1 = 0.4787 \\ ROUGE-3 = 0.1652 \\ ROUGE-1 = 0.4241 \\ \end{tabular}  & \begin{tabular}{c} Reported better topic coverage \\ and conveyed more diverse \\ user emotions. \\ \end{tabular}  \\ \hline
[39] & \begin{tabular}{c} Credibility, \\ worthiness, \\ accuracy \\ \end{tabular}  & Supervised & Disaster events & \begin{tabular}{c} PHEME dataset \\ \end{tabular}  & \ \begin{tabular}{c} ROUGE-1 = 0.501 \\ \end{tabular}  & \begin{tabular}{c} Extractive technique for \\ summarizing tweets with the \\ task rumor detection. \\ \end{tabular}  \\ \hline
[66] & \begin{tabular}{c} Coverage and \\ sparsity, diversity, \\ and redundancy \\ \end{tabular}  & \begin{tabular}{c} Social \\ Network and \\ Separation (SNSR) \\ \end{tabular}  & Generic &