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Natural language processing (NLP)




Python, NLTK, spaCy

Skills to be Learned:

Proficiency in using NLP libraries (NLTK, spaCy) for text processing

Text Summarization

Dive into NLP with our "Text Summarization on CNN/Daily Mail Dataset" course. Master extractive text summarization using Python and popular NLP libraries, and learn to condense large texts into coherent summaries.

The "Text Summarization on CNN/Daily Mail Dataset" course is a practical and hands-on learning experience for individuals interested in natural language processing (NLP) and text summarization. In this project-based course, participants will dive into the world of automatic text summarization using Python and popular NLP libraries. The primary focus will be on creating extractive text summarization models to generate concise and coherent summaries from large articles or documents. By the end of the course, students will have the skills to develop and evaluate text summarization models, a valuable asset for anyone working with large volumes of textual information.

Learning Outcomes:

Upon completing this course, participants will:

- Understand the concepts and techniques behind text summarization.

- Gain hands-on experience with Python, NLP libraries, and machine learning for text summarization.

- Learn to preprocess and prepare textual data for summarization tasks.

- Develop extractive text summarization models that automatically identify and extract important sentences from articles.

- Evaluate the quality of generated summaries using appropriate metrics.

- Apply text summarization techniques to real-world datasets like the CNN/Daily Mail dataset.


- Basic programming skills in Python.

- Familiarity with fundamental NLP concepts, though not mandatory, will be helpful.

- Access to a Python environment with the required libraries for NLP and machine learning.

Libraries and Programming Language Used:

- Python for coding and scripting.

- Popular NLP libraries such as NLTK or spaCy for text processing.

- Machine learning libraries such as scikit-learn or TensorFlow for building summarization models.

Course Syllabus:

Introduction to Text Summarization

   - Understanding the importance and applications of text summarization.

   - Overview of extractive vs. abstractive summarization techniques.

Setting Up the Development Environment

   - Installing and configuring the necessary Python libraries.

   - Preparing the development environment for text summarization tasks.

Data Acquisition and Preprocessing

   - Obtaining textual data, particularly the CNN/Daily Mail dataset.

   - Cleaning, tokenization, and preprocessing of text documents.

Extractive Text Summarization Models

   - Introduction to extractive summarization methods.

   - Implementing algorithms to identify important sentences in documents.

Feature Engineering for Summarization

   - Extracting informative features from text for summarization.

   - Building feature vectors for sentences.

Model Training and Evaluation

   - Training extractive summarization models using machine learning techniques.

   - Evaluating the quality of generated summaries using metrics like ROUGE.

Real-World Application

   - Applying text summarization models to real-world articles or documents.

   - Summarizing news articles from the CNN/Daily Mail dataset as a practical example.

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