# Automatic Short Text Summarization Techniques in Social Media Platforms
## 7 ASTS Techniques for Social Media Platforms
To evaluate the quality of their generated summaries, the researchers compared them with results from other text summarization algorithms like LexRank, TextRank, Latent Semantic Analysis, SumBasic, and KL-Sum. They used a dataset of Twitter comments related to the presidential election in Indonesia, collected using the hashtag #pilpres. The results demonstrated that MOACO was capable of producing informative and concise summaries. The average cosine distance to the source text was 0.127, indicating good retention of information, and the average number of words in the summaries was 388, which outperformed the other algorithms. The paper by [PERSON] et al. introduces a novel selective reinforced sequence-to-sequence attention model for abstractive social media text summarization [55]. The proposed model incorporates a hidden layer before the encoder module and utilizes a selective gate to determine which information should be retained or discarded. Moreover, the model combines cross-entropy and reinforcement learning policy to directly optimize the ROUGE score. The experiments were conducted on the LCSTS dataset, which was obtained from Sina Weibo. This dataset is composed of three parts: PART I with 2,400,591 text-summary pairs, PART II with 10,666 text-summary pairs, and PART III with 1106 pairs. The evaluation results demonstrated the effectiveness of the proposed model, with F1 scores of 38.2 for ROUGE-1, 25.2 for ROUGE-2, and 35.5 for ROUGE-L. In the paper authored by [PERSON] and [PERSON], a novel graph-based framework was introduced to generate storylines on Twitter [56]. This innovative approach takes into account both the semantic content and social information of tweets. The storyline is represented as a directed tree, showcasing socially salient events that unfold over time. In this representation, nodes signify main events, while edges capture the semantic relationships between related events. To identify significant sub-events, they employed a community detection technique on a temporal and semantic similarity graph. Extensive experiments were conducted on real-world events in both English and Persian datasets extracted from Twitter. The performance of their event summarization module was evaluated using ROUGE 1 and ROUGE 2 metrics, with achieved values of 0.6239 and 0.5476, respectively. In their work, [PERSON] et al. presented a two-phase summarization approach to create abstract summaries for Twitter events [57]. Initially, the approach extracts key sentences from a pool of tweets relevant to the event, effectively reducing redundant information by leveraging Partial Textual Entailment (PTE) relations between sentences. Subsequently, an abstract summary is generated using the least redundant key sentences. The proposed approach's performance was evaluated through experiments on various datasets. The data encompassed English tweets related to 25 trending events collected from Twitter4j during the period from January to October 2017. These events covered diverse topics such as natural disasters, politics, sports, entertainment, and technology. The evaluation results were measured using ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-SU4 metrics, yielding scores of 0.49, 0.38, 0.45, and 0.39, respectively. [PERSON] et al. proposed a clustering approach for micro-blogs that combines feature selection techniques to group similar messages [58]. The approach effectively reduces information overload and addresses the problem of less content and noisy messages. The best performance of the proposed methodology according to each metric was as follows: 43.4864 for the Calinski-Harabasz (CH) index, 0.795 for the DB (DB) index, 1.52 for the Dunn (D) index, 0.1425 for the I-Index (I), 0.2708 for the Silhouette (S) index, and 0.1319 for the Xie-Beni (XB) on Hagupit dataset. In the study conducted by [PERSON] et al., they presented a graph-based approach to summarize tweets [59].