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+# Text Summarization
+
+### AIM
+Develop a model to summarize long articles into short, concise summaries.
+
+### DATASET LINK
+[CNN DailyMail News Dataset](https://www.kaggle.com/datasets/gowrishankarp/newspaper-text-summarization-cnn-dailymail/)
+
+### NOTEBOOK LINK
+- [Text-Summarization using TF-IDF](https://www.kaggle.com/code/piyushchakarborthy/txt-summarization-using-tf-idf)
+- [Text-Summarization using Transformers](https://www.kaggle.com/code/piyushchakarborthy/text-summarization-using-transformer)
+- [Text-Summarization using TextRank](https://www.kaggle.com/code/piyushchakarborthy/text-summarization-using-textrank)
+
+### LIBRARIES NEEDED
+??? quote "LIBRARIES USED"
+
+    - pandas
+    - numpy
+    - scikit-learn
+    - matplotlib
+    - keras
+    - tensorflow
+    - spacy
+    - pytextrank
+    - TfidfVectorizer
+    - Transformer (Bart)
+--- 
+
+### DESCRIPTION
+
+??? info "What is the requirement of the project?"
+    - A robust system to summarize text efficiently is essential for handling large volumes of information.
+    - It helps users quickly grasp key insights without reading lengthy documents.
+
+??? info "Why is it necessary?"
+    - Large amounts of text can be overwhelming and time-consuming to process.
+    - Automated summarization improves productivity and aids decision-making in various fields like journalism, research, and customer support.
+
+??? info "How is it beneficial and used?"
+    - Provides a concise summary while preserving essential information.
+    - Used in news aggregation, academic research, and AI-powered assistants for quick content consumption.
+
+??? info "How did you start approaching this project? (Initial thoughts and planning)"
+    - Explored different text summarization techniques, including extractive and abstractive methods.
+    - Implemented models like TextRank, BART, and T5 to compare their effectiveness.
+
+??? info "Mention any additional resources used (blogs, books, chapters, articles, research papers, etc.)."
+    - Documentation from Hugging Face Transformers
+    - Research Paper: "Text Summarization using Deep Learning"
+    - Blog: "Introduction to NLP-based Summarization Techniques"
+
+---
+
+#### DETAILS OF THE DIFFERENT FEATURES
+The dataset contains features like sentence importance, word frequency, and linguistic structures that help in generating meaningful summaries.
+
+| Feature              | Description                                     |
+|----------------------|-------------------------------------------------|
+| `sentence_rank`        | Rank of a sentence based on importance using TextRank |
+| `word_freq`            | Frequency of key terms in the document |
+| `tf-idf_score`         | Term Frequency-Inverse Document Frequency for words |
+| `summary_length`       | Desired length of the summary |
+| `generated_summary`    | AI-generated condensed version of the original text |
+
+---
+
+#### PROCEDURE
+
+=== "Step 1"
+
+    Exploratory Data Analysis:
+
+    -  Loaded the CNN/DailyMail dataset using pandas.
+    -  Explored dataset features like article and highlights, ensuring the correct format for summarization.
+    -  Analyzed the distribution of articles and their corresponding summaries.
+
+=== "Step 2"
+
+    Data cleaning and preprocessing:
+
+      - Removed unnecessary columns (like id) and checked for missing values.
+      - Tokenized articles into sentences and words, removing stopwords and special characters.
+      - Preprocessed the text using basic NLP techniques such as lowercasing, lemmatization, and removing non-alphanumeric characters.
+
+=== "Step 3"
+
+    Feature engineering and selection:
+
+      - For TextRank-based summarization, calculated sentence similarity using TF-IDF (Term Frequency-Inverse Document Frequency) and Cosine Similarity.
+      - Selected top-ranked sentences based on their importance and relevance to the article.
+      - Applied transformers-based models like BART and T5 for abstractive summarization.
+      - Applied transformers-based models like BART and T5 for abstractive summarization.
+
+=== "Step 4"
+
+    Model training and evaluation:
+
+      - For the TextRank summarization approach, created a similarity matrix based on TF-IDF and Cosine Similarity.
+      - For transformer-based methods, used Hugging Face's BART and T5 models, summarizing articles with their pre-trained weights.
+      - Evaluated the summarization models based on BLEU, ROUGE, and Cosine Similarity metrics.
+
+=== "Step 5"
+
+    Validation and testing:
+
+      - Tested both extractive and abstractive summarization models on unseen data to ensure generalizability.
+      - Plotted confusion matrices to visualize True Positives, False Positives, and False Negatives, ensuring effective model performance.
+---
+
+#### PROJECT TRADE-OFFS AND SOLUTIONS 
+
+=== "Trade-off 1"
+
+    Training Dataset being over 1.2Gb, which is too large for local machines.
+
+      - **Solution**: Instead of Training a model on train dataset, Used Test Dataset for training and validation.
+
+=== "Trade-off 2"
+
+    Transformer models (BART/T5) required high computational resources and long inference times for summarizing large articles.
+
+      - **Solution**: Model Pruning: Used smaller versions of transformer models (e.g., distilBART or distilT5) to reduce the computational load without compromising much on performance.
+
+=== "Trade-off 2"
+
+    TextRank summary might miss nuances and context, leading to less accurate or overly simplistic outputs compared to transformer-based models.
+
+      - **Solution**: Combined TextRank and Transformer-based summarization models in a hybrid approach to leverage the best of both worlds—speed from TextRank and accuracy from transformers.
+
+
+--- 
+
+### SCREENSHOTS
+
+!!! success "Project flowchart"
+
+    ``` mermaid
+      graph LR
+    A[Start] --> B[Load Dataset]
+    B --> C[Preprocessing]
+    C --> D[TextRank + TF-IDF / Transformer Models]
+    D --> E{Compare Performance}
+    E -->|Best Model| F[Deploy]
+    E -->|Retry| C;
+    ```
+
+??? example "Confusion Matrix"
+
+    === "TF-IDF Confusion Matrix"
+        ![tfidf](https://github.com/user-attachments/assets/28f257e1-2529-48f1-81e5-e058a50fb351)
+        
+    === "TextRank Confusion Matrix"
+        ![textrank](https://github.com/user-attachments/assets/cb748eff-e4f3-4096-ab2b-cf2e4b40186f)
+
+    === "Transformers Confusion Matrix"
+        ![trans](https://github.com/user-attachments/assets/7e99887b-e225-4dd0-802d-f1c2b0e89bef)
+
+
+### CONCLUSION 
+
+#### KEY LEARNINGS 
+
+!!! tip "Insights gained from the data"
+    - Data Complexity: News articles vary in length and structure, requiring different summarization techniques.
+    - Text Preprocessing: Cleaning text (e.g., stopword removal, tokenization) significantly improves summarization quality.
+    - Feature Extraction: Techniques like TF-IDF, TextRank, and Transformer embeddings help in effective text representation for summarization models.
+
+??? tip "Improvements in understanding machine learning concepts"
+    - Model Selection: Comparing extractive (TextRank, TF-IDF) and abstractive (Transformers) models to determine the best summarization approach.
+
+??? tip "Challenges faced and how they were overcome"
+    - Long Text Processing: Splitting lengthy articles into manageable sections before summarization.
+    - Computational Efficiency: Used batch processing and model optimization to handle large datasets efficiently.
+
+--- 
+
+#### USE CASES 
+
+=== "Application 1"
+
+    **News Aggregation & Personalized Summaries**
+    
+      - Automating news summarization helps users quickly grasp key events without reading lengthy articles.
+      - Used in news apps, digital assistants, and content curation platforms.
+
+=== "Application 2"
+
+    **Legal & Academic Document Summarization**
+    
+      - Helps professionals extract critical insights from lengthy legal or research documents.
+      - Reduces the time needed for manual reading and analysis.
+