# Automatic Short Text Summarization Techniques in Social Media Platforms
## 8 Discussion

Table 2: _Cont._for further exploration in this area. Some topics of ASTS require greater attention in future research endeavors:
* Extractive-based ASTS techniques encompass a range of methodologies, including machine learning and optimization-based, statistical-based, graph-based, and fuzzy-logic-based approaches. Within the machine learning realm, both supervised and unsupervised techniques aim to enhance accuracy [67] quality [52; 58; 63; 65], and similarity [42; 59]. Yet, a comprehensive evaluation often requires a broader focus, encompassing diversity, relevancy, completeness, readability, and redundancy. * Supervised classification techniques, although effective, demand substantial training datasets, leading to challenges in their creation. Unsupervised methods, while promising, encounter the complexities inherent in the diversity and noise of tweets. Optimization-based techniques seek to optimize redundancy [44; 54; 66], diversity, coverage [67], and other objectives. However, their potential to enhance summary quality, accuracy, similarity, sparsity, and computational efficiency deserves deeper exploration. Resolving conflicting objectives should involve multi-objective optimization algorithms. * Considerations in statistical and graph-based ASTS techniques
* The landscape of automatic short text summarization (ASTS) techniques is enriched by the contributions of both statistical-based and graph-based approaches. However, a nuanced evaluation reveals distinct considerations and priorities within each of these methodologies. Statistical-based techniques emphasize summary quality and relevancy yet often overlook diversity, accuracy, readability, time constraints, and computational costs. In contrast, graph-based methods prioritize diversity, accuracy [1], and performance [12], with less emphasis on quality, similarity, completeness, redundancy, coverage, readability, time, and computational costs. As ASTS techniques continue to evolve, a synthesis of statistical-based and graph-based methodologies holds the potential to elevate summarization outcomes to new heights. By integrating the strengths of both approaches, researchers could create a more versatile and balanced framework that not only prioritizes quality, diversity, and accuracy but also addresses other critical dimensions. A subset of ASTS techniques dabbles in the application of fuzzy logic; however, the extent of its effectiveness remains uncertain. Further exploration is warranted to ascertain its potential contribution to the advancement of ASTS methodologies. * Balancing act for optimal summarization
* In the pursuit of effective summarization, the choice between extractive, abstractive, or hybrid approaches hinges on the nature of the input data, the desired level of summary coherence, the intended application, and the complexity of the summarization task. Extractive methods often excel when preserving source text information is crucial, while abstractive methods shine when generating concise and engaging summaries is paramount. However, it is the hybrid approach that embodies the quest for a harmonious equilibrium. By integrating the strengths of both extractive and abstractive techniques, hybrids aspire to deliver summaries that capture the core essence of the source while infusing them with creative and contextual innovations. The realm of ASTS is not about choosing one approach over the other, but rather about finding the ideal equilibrium that aligns with the summarization goals. This dynamic interplay ensures that the art of summarization continues to evolve, offering versatile solutions that adapt to the nuances of diverse text inputs and user preferences. * Dataset limitations and multi-lingual evaluation
* The efficacy of automatic short text summarization (ASTS) techniques is inherently tied to the quality and diversity of the datasets used for evaluation.