# Automatic Short Text Summarization Techniques in Social Media Platforms
## 7 ASTS Techniques for Social Media Platforms  
The literature review section initiates by providing a comprehensive overview of research studies focusing on automatic short text summarization (ASTS) techniques tailored specifically for social media platforms. Within this review, we delve into the two primary categories of ASTS techniques: extractive and abstractive summarization. Additionally, we explore novel hybrid approaches that cleverly combine both extractive and abstractive methods, harnessing their respective strengths synergistically. To facilitate a better understanding and comparison, Table 2 offers a comprehensive summary with a comparative analysis of ASTS Techniques in the realm of social media. This table serves as a valuable resource for quickly assessing key metrics and outcomes, empowering researchers and practitioners to make informed decisions while selecting the most suitable ASTS techniques for their social media applications. In their recent work, [33] introduced an innovative method that utilizes advanced tools like a state-of-the-art open-sourced search engine and a large language model to generate accurate and comprehensive summaries. By gathering information from social media and online news sources, their approach incorporates cutting-edge search strategies and leverages GPT-3 in a one-shot setting for summarizing crisis-related content. The method's evaluation took place on the TREC CrisisFACTS challenge dataset [34], which encompasses data from diverse platforms such as Twitter, Facebook, Reddit, and online news sources. The assessment involved the use of both automatic summarization metrics (ROUGE-2 and BERTScore) and manual evaluations conducted by the challenge organizers. Despite having a high redundancy ratio, the manual evaluations revealed the generated summaries to be remarkably comprehensive. Notably, the best automatic evaluation results showcased an impressive BERTScore of 0.4591 and a ROUGE-2 score of 0.0581 on the ICS 209 dataset. In their paper, [PERSON] and [PERSON] addressed the challenge of summarizing comments related to the COVID-19 pandemic from a collection of Twitter comments [35]. Their approach involved several steps to achieve summarization. Firstly, they initiated the summarization task by clustering topics using the latent Dirichlet allocation (LDA) method and K-means clustering. This allowed them to identify relevant topics in the comments. To handle topic overlaps between multiple comments, they employed K-mean clustering to group similar phrases together. Next, they implemented the TF-IDF ranking algorithm based on context similarity to select important sentences for creating the summary. This ensured that the most relevant and significant information was included in the summarization. To determine the optimal number of topics for training LDA, they utilized the c_v topic coherence score. Moreover, they employed the Silhouette metric to measure the convergence range of data points around the center points in each cluster. For evaluation purposes, the researchers collected around 100,000 tweets between 1 March and 31 July 2022, based on predefined conditions and parameters. To assess the performance of their proposed method, they employed the ROUGE measure for various numbers of LDA topics, obtaining the following scores:  
* ROUGE-1: (F1 score: 0.06 to 0.26, recall: 0.1 to 0.21, and precision: 0.05 to 0.19)
* ROUGE-2: (F1 score: 0.04 to 0.23, recall: 0.01 to 0.19, and precision: 0.03 to 0.17)
* ROUGE-S4: (F1 score: 0.04 to 0.21, recall: 0.01 to 0.18, and precision: 0.03 to 0.15)
* ROUGE-SU4: (F1 score: 0.29 to 0.48, recall: 0.04 to 0.07, and precision: 0.07 to 0.12)  
[PERSON] et al. conducted a study in which they designed and implemented a crawler to extract popular text posts from Instagram [36]. The extracted posts underwent appropriate pre-processing, and a combination of extraction and abstraction algorithms was applied to demonstrate the usefulness of each abstraction algorithm.