# Automatic Short Text Summarization Techniques in Social Media Platforms
## 7 ASTS Techniques for Social Media Platforms
The first step involved constructing a tweet similarity graph, followed by the application of community detection methodology to group together similar tweets. To represent the tweet summarization, important tweets were identified from each cluster using various graph centrality measures, including degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, and PageRank. To perform their experiments, the researchers collected a dataset consisting of 2921 tweets from Twitter API using a crawler. The performance of their proposed methodology was evaluated using precision, recall, and F-measure metrics. The obtained results showed a precision of 0.957, recall of 0.937, and F-measure of 0.931, demonstrating the effectiveness of their approach in tweet summarization. Ref. [60] presented a regularization approach for the sequence-to-sequence model, specifically tailored for the Chinese social media summarization task. Their method involved leveraging the model's learned knowledge to apply regularization to the learning objective, effectively mitigating summarization-related challenges. To enhance the evaluation process and ensure semantic consistency, they introduced a practical human evaluation approach, addressing the limitations of existing automatic evaluation methods. For their experiments, the researchers utilized the LCSTS dataset. This dataset comprises over 2.4 million text-summary pairs extracted from the popular Chinese social media microblogging service, Weibo. The automatic evaluation of the proposed method yielded the following results: ROUGE-1 score of 36.2, ROUGE-2 score of 24.3, and ROUGE-L score of 33.8. These results demonstrate the effectiveness of their regularization approach in the context of Chinese social media summarization. In their work, [PERSON] et al. introduced an approach for microblog summarization, which leverages the Paragraph Vector and semantic structure. The method comprises several key steps [61]. Firstly, they constructed a sentence similarity matrix, capturing the contextual information of microblogs, to identify sub-topics using the Paragraph Vector. Next, they analyzed the sentences using the Chinese Sentential Semantic Model (CSM) to extract semantic features. Additionally, relation features were derived from the combination of the similarity matrix and the aforementioned semantic features. Lastly, the most informative sentences were accurately selected from microblogs belonging to the same sub-topics based on both semantic and relation features. To evaluate their method, the researchers created an evaluation dataset consisting of 16 microblog topics. These topics were collected from the 2013 NLP&CC (CCF Conference on Natural Language Processing & Chinese Computing) conference database and Chinese microblog data gathered online. The proposed method achieved ROUGE scores as follows: 0.531706 for ROUGE-1, 0.265799 for ROUGE-2, and 0.266479 for ROUGE-SU. [PERSON] et al. presented a method to summarize tweets related to news articles [62]. The approach revolves around capturing diverse opinions expressed in the tweets, which is achieved through the creation of a unique TSG (Twitter Sentiment Graph). By utilizing a community detection technique on this TSG, the researchers successfully identify tweets that represent these diverse opinions. Their findings demonstrate that the initial capturing of diverse opinions significantly enhances the accuracy of identifying the relevant set of tweets. To evaluate the effectiveness of their approach, the researchers utilized a dataset comprising political news articles sourced from the New York Post, along with corresponding Twitter data. The evaluation of their proposed method was carried out using ROUGE-I and ROUGE-II metrics, with the following scores obtained: ROUGE-I--precision of 0.67, recall of 0.66, and F1 score of 0.664; ROUGE-II--precision of 0.56, recall of 0.358, and F1 score of 0.548. [PERSON] et al. put forth a classification-based summarization approach, catering to both English and Hindi tweets [63].