Context-Aware Topic Modeling and Intelligent Text Extraction Using Transformer-Based Architectures

In the era of information overload, extracting meaningful insights from unstructured textual data has become a critical task in numerous domains, including digital humanities, social media analysis, legal discovery, and enterprise content management. Traditional topic modeling techniques such as Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) often fall short in capturing semantic nuance and contextual dependencies, especially when dealing with heterogeneous and domain-specific corpora. This paper presents a novel framework that leverages transformer-based architectures to enable context-aware topic modeling and intelligent text extraction, offering a significant advancement in the interpretability, granularity, and adaptability of topic detection and content summarization. Our approach is built upon pretrained transformer models, notably BERT and its domain-specific derivatives, which have demonstrated superior performance in a wide range of natural language processing (NLP) tasks due to their deep contextual representation capabilities. We introduce a hybrid pipeline that combines dynamic embedding generation with unsupervised clustering and contrastive learning to enhance topic coherence and interpretability. Rather than relying solely on word frequency or co-occurrence, our method captures high-dimensional semantic relationships between tokens and sentences, enabling a more refined and context-sensitive delineation of topics across document sets. A key component of our framework is the intelligent text extraction module, designed to identify and summarize the most salient information from lengthy or complex documents. Using attention-based mechanisms and fine-tuned extractive summarization models such as BART and PEGASUS, the system can prioritize content based on thematic relevance, user-defined criteria, or contextual cues. This facilitates adaptive content retrieval and targeted summarization, supporting a wide range of use cases including automated report generation, content curation, and knowledge base construction. To evaluate the efficacy of the proposed system, we conducted experiments on benchmark datasets such as 20 Newsgroups, ArXiv abstracts, and multi-domain news articles, as well as proprietary datasets from the legal and healthcare domains. Our results demonstrate marked improvements over classical topic modeling baselines in terms of topic coherence (measured by NPMI and UMass scores), clustering accuracy (using ARI and NMI), and summarization quality (evaluated with ROUGE and BLEU metrics). Furthermore, qualitative analyses reveal that the generated topics align more closely with human interpretations, particularly in domain-specific settings where contextual understanding is crucial. In addition to its performance benefits, the proposed architecture offers scalability and adaptability. By integrating transformer-based embeddings with dimensionality reduction techniques such as UMAP or t-SNE, and lightweight clustering algorithms like HDBSCAN or KMeans++, the system achieves efficient processing of large-scale document collections without compromising output quality. Furthermore, the modularity of the pipeline allows for domain-specific customization through transfer learning, enabling the integration of user feedback or ontological constraints to guide topic interpretation and extraction strategies. We conclude that context-aware topic modeling and intelligent text extraction using transformer-based architectures represent a paradigm shift in automated text analysis. By marrying the deep semantic understanding of transformers with interpretable modeling techniques, our framework provides a robust, scalable, and versatile solution for deriving actionable insights from unstructured text. Future work will focus on enhancing model explainability, incorporating multilingual capabilities, and exploring real-time deployment scenarios in knowledge-intensive applications.

Context-aware topic modeling, transformer-based architectures, intelligent text extraction, BERT, deep contextual embeddings, extractive summarization, semantic clustering, NLP, unsupervised learning, attention mechanisms, document summarization, topic coherence, content retrieval, scalable text analysis, domain-specific NLP

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