Development of a Natural Language Processing System for Automated Content Summarization
Table Of Contents
<p>1. Introduction<br> - Background and Motivation<br> - Objectives<br> - Scope and Significance<br>2. Literature Review<br> - Automated Content Summarization<br> - NLP Techniques for Summarization<br> - Existing Summarization Systems<br>3. Text Processing and Analysis<br> - Text Preprocessing and Tokenization<br> - Semantic Analysis and Information Extraction<br> - Language Modeling and Coherence<br>4. NLP System Design and Architecture<br> - Summarization Algorithms and Models<br> - User Interface and Interaction<br> - Integration with Content Sources<br>5. System Implementation and Integration<br> - NLP Libraries and Frameworks<br> - Data Processing Pipelines<br> - Integration with Content Platforms<br></p>
Thesis Abstract
<p> This project aims to develop a natural language processing (NLP) system for automated content summarization that can generate concise and coherent summaries of textual content. The system will leverage NLP techniques, such as text summarization algorithms, semantic analysis, and language modeling, to automatically extract key information from documents, articles, and other textual sources. The project will address the challenges of automated content summarization, such as information extraction, coherence, and readability, by designing and implementing an NLP system that can produce high-quality and informative summaries across diverse content domains. <br></p>Thesis Overview
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