Revolutionizing Text Summarization: A Hybrid Approach with Neural Networks and Pre Trained Models

Enhancing the Quality of Text Summarization: A Comprehensive Study

Abstract:

In recent years, text summarization has emerged as an essential technique in information retrieval and processing. This paper presents a detled analysis of current advancements and challenges in this field. We propose a novel method that enhance the quality of summaries by existing text-based systems.

Introduction:

Text summarization involves creating concise versions of lengthy documents or articles, providing readers with an overview of key points without losing significant information. As digital content continues to expand at an exponential rate, effective text summarization becomes increasingly crucial for efficient information management and understanding.

Existing Solutions:

Traditional methods typically employ techniques such as extractive summarization which selects the most relevant sentences based on statistical measures or abstractive summarization which synthesizes new sentences using deep learning. However, these approaches face limitations in capturing context coherence, preserving tone and nuance, and handling complex structures.

Proposed Method:

Our proposed method combines elements of both extractive and abstractive summarization while incorporating a novel approach to enhance and informativeness. It leverages state-of-the-art neural networks trned on large datasets for automatic extraction of key information points. Subsequently, it utilizes pre-trned languageto refine the summary, ensuring coherence, fluency, and semantic accuracy.

Benefits:

The integration of extractive techniques ensures that essential facts are included in the summary, while abstractive components introduce adaptability by generating new sentences that capture the essence of the original text. This dual approach addresses common issues faced by traditional summarization methods, such as lack of context coherence and reduced .

:

In , this paper outlines a comprehensive strategy to improve the quality of summaries by modern text-based systems. By combining strengths from both extractive and abstractive techniques while integrating advanced language processing, we propose a method that significantly enhances summary effectiveness, making it a valuable tool for various applications in information retrieval and processing.

References:

Include relevant literature on text summarization methods and advancements, such as papers discussing neural network architectures, pre-trned language, and evaluation metrics for summarization performance.
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