Enhanced Methodology for Optimal Text Clustering and Summarization

An Enhanced Methodology for Improving Text Clustering and Summarization

Abstract:

In this paper, we present an innovative approach med at enhancing the efficiency of text clustering and summarization tasks. The existing methodologies often fl to achieve optimal outcomes due to limitations in their algorithms or inability to capture the complex nuances within textual data. Our proposed addresses these shortcomings by introducing advanced techniques for feature extraction, cluster evaluation, and summarization algorithms.

The core innovation lies in our feature extraction process which leverages state-of-the-art text embeddingthat are capable of capturing multi-dimensional semantic information from texts. This provides a richer representation compared to traditional methods like TF-IDF or word embeddings alone.

In the clustering phase, we implement an enhanced version of the K-Means algorithm with a dynamic threshold mechanism based on inter-cluster and intra-cluster distance optimization. The algorithm automatically adjusts its parameters according to the data distribution, ensuring that clusters are neither too large nor too small but rather well-balanced for optimal performance.

For summarization, we employ a hierarchical agglomerative clustering technique combined with an evaluation metric that considers both relevance and coherence of sentences in the summary. This ensures that summaries are not only concise but also informative and accurately reflect the essence of the text.

Experiments conducted on various datasets demonstrate significant improvements over existing methods in terms of F1 score, purity of clusters, and quality of summaries, proving the effectiveness and robustness of our proposed approach across different domns.

, by integrating advanced feature extraction, optimized clustering algorithms, and advanced summarization techniques, we have significantly enhanced the capabilities for text analysis tasks. This not only improves efficiency but also enhances accuracy in a variety of applications requiring detled textual insights.

Keywords: Text Clustering, Summarization, Feature Extraction, Algorithms
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