Automatic Generation of Summaries From Informal Social Media Text Using Transformers

ABSTRACT The exponential growth of social media has reshaped global communication and decision‐making in business, politics and economics. Yet, the sheer volume and informal, unstructured nature of user‐generated content present major challenges for meaningful analysis. This study introduces a novel abstractive summarisation framework designed to distill coherent and semantically rich summaries from social media discussions. Built on the T5 transformer architecture and enhanced through targeted transfer learning, the system effectively captures the fragmented, slang‐rich language patterns common across platforms. Evaluation is conducted using a suite of semantic‐aware metrics—including ROUGE‐WE, SUPERT and Shannon entropy—alongside human‐centric criteria such as coherence, fluency, consistency and lexical diversity. Results show that the proposed model consistently outperforms mainstream summarisation methods, including advanced systems like ChatGPT, particularly in preserving semantic alignment and improving readability under noisy conditions. Comparative analysis underscores the framework's robustness in handling unstructured, domain‐specific discourse. These findings position the model as a valuable tool for real‐time, high‐volume social media analytics. Future work will explore hybrid neural quality assessment and interactive feedback mechanisms to further enhance domain adaptability and summary fluency.