# Automatic Short Text Summarization Techniques in Social Media Platforms
## 4 ASTS Structure
 & [[https://paperswithcode.com/dataset/losts](https://paperswithcode.com/dataset/losts)]([https://paperswithcode.com/dataset/losts](https://paperswithcode.com/dataset/losts)) (accessed on 7 August 2023) \\ \hline TwitterAAE dataset 2016 & Twitter—English & 60 million tweets & [[http://danglab.cs.umnes.edu/TwitterAAE](http://danglab.cs.umnes.edu/TwitterAAE)]([http://danglab.cs.umnes.edu/TwitterAAE](http://danglab.cs.umnes.edu/TwitterAAE)) (accessed on 7 August 2023) \\ \hline CrowdFlower AI Gender dataset 2016 & Gender classifier data & Twitter—English & 20,000 posts with user-information & [[https://www.crowdflower.com/data-for-everyone/](https://www.crowdflower.com/data-for-everyone/)]([https://www.crowdflower.com/data-for-everyone/](https://www.crowdflower.com/data-for-everyone/)) (accessed on 7 August 2023) \\ \hline USPPresidentialDebates 2016 & Us election & Twitter—English & 11 million tweets & [[https://www.kaggle.com/mnrisdal/2016-us-presidential-debates](https://www.kaggle.com/mnrisdal/2016-us-presidential-debates)]([https://www.kaggle.com/mnrisdal/2016-us-presidential-debates](https://www.kaggle.com/mnrisdal/2016-us-presidential-debates)) (accessed on 7 August 2023) \\ \hline Twitter events 2012–2016 & \ \begin{tabular}{c} different \\ real-world events \\ \end{tabular}  & Twitter—English & 147 million tweets &
\begin{tabular}{c} [[https://figshare.com/articles/Twitter](https://figshare.com/articles/Twitter)]([https://figshare.com/articles/Twitter](https://figshare.com/articles/Twitter)). \\ event, datasets, 2012-20161. Data collection and pre-processing: Data collection: Gather short text data from social media platforms or other sources, depending on the application. Data pre-processing: Clean and preprocess the collected data, which includes text cleaning, tokenization, stopword removal, lemmatization, stemming, and sentence segmentation. 2. Feature extraction: Extract relevant features from the preprocessed data to represent the content for the summarization model. Features can include TF-IDF vectors, word embeddings, or contextual embeddings generated using pre-trained language models like BERT or GPT. 3. Summarization model: Extractive summarization: For extractive summarization, use techniques such as graph-based algorithms (e.g., TextRank) [25], attention mechanisms (e.g., Transformer-based models), or neural network-based methods (e.g., LSTM) to identify and select important sentences or phrases from the input text to form the summary. Abstractive summarization: For abstractive summarization, employ neural network models, such as sequence-to-sequence architectures (e.g., LSTM with attention, Transformer-based models like BART), to generate new sentences that convey the essential information not necessarily present in the original text. 4. Evaluation: Measure the quality and performance of the summarization model using evaluation metrics such as ROUGE (Recall-Oriented Understudy for Gisting Evaluation), BLEU (Bilingual Evaluation Understudy), or other domain-specific metrics. These metrics compare the generated summaries with human-written summaries or ground truth summaries. 5. Fine-tuning and optimization: Fine-tune the summarization model on a domain-specific or task-specific dataset to improve its performance and adapt it to the target domain. Optimize hyperparameters and model architecture through experimentation and tuning. 6. Deployment: Integrate the trained ASTS model into a larger application or platform to provide automatic short text summarization functionality to users. Monitor and maintain the system in production to ensure its continued accuracy and effectiveness.