Social media’s explosive growth has resulted in a massive influx of electronic documents influencing various facets of daily life. However, the enormous and complex nature of this content makes extracting valuable insights challenging. Long document summarization emerges as a pivotal technique in this context, serving to distill extensive texts into concise and comprehensible summaries. This paper presents a novel three-stage pipeline for effective long document summarization. The proposed approach combines unsupervised and supervised learning techniques, efficiently handling large document sets while requiring minimal computational resources. Our methodology introduces a unique process for forming semantic chunks through spectral dynamic segmentation, effectively reducing redundancy and repetitiveness in the summarization process. Contrary to previous methods, our approach aligns each semantic chunk with the entire summary paragraph, allowing the abstractive summarization model to process documents without truncation and enabling the summarization model to deduce missing information from other chunks. To enhance the summary generation, we utilize a sophisticated rewrite model based on Bidirectional and Auto-Regressive Transformers (BART), rearranging and reformulating summary constructs to improve their fluidity and coherence. Empirical studies conducted on the long documents from the Webis-TLDR-17 dataset demonstrate that our approach significantly enhances the efficiency of abstractive summarization transformers. The contributions of this paper thus offer significant advancements in the field of long document summarization, providing a novel and effective methodology for summarizing extensive texts in the context of social media.