Text Summarization Tool

 Text Summarization Tool 

1. Introduction

Objective: Build a tool for summarizing text using either extractive or abstractive summarization techniques.
Purpose: Enable concise and meaningful representation of large textual content for improved readability and insight extraction.

2. Project Workflow

1. Problem Definition:
   - Summarize lengthy text content while retaining its core meaning.
   - Key questions:
     - Which summarization technique (extractive or abstractive) is suitable for the dataset?
     - What metrics should be used to evaluate summarization quality?
2. Data Collection:
   - Source: News articles, research papers, or public datasets like CNN/Daily Mail.
3. Data Preprocessing:
   - Tokenization, stopword removal, and handling special characters.
4. Model Selection:
   - Extractive: TextRank or frequency-based techniques.
   - Abstractive: Transformer-based models like BART or T5.
5. Evaluation:
   - Compare summaries using ROUGE or BLEU scores.

3. Technical Requirements

- Programming Language: Python
- Libraries/Tools:
  - NLP: NLTK, SpaCy, gensim
  - Machine Learning: Hugging Face Transformers, PyTorch, TensorFlow
  - Data Handling: Pandas, NumPy

4. Implementation Steps

Step 1: Setup Environment

Install required libraries:
```
pip install pandas numpy nltk spacy transformers torch tensorflow
```
Download NLP resources:
```
import nltk
nltk.download('punkt')
nltk.download('stopwords')
```

Step 2: Load and Preprocess Data

Load the dataset:
```
import pandas as pd

data = pd.read_csv("text_summarization_dataset.csv")
print(data.head())
```
Preprocess text data:
```
from nltk.corpus import stopwords
from nltk.tokenize import sent_tokenize, word_tokenize

def preprocess(text):
    sentences = sent_tokenize(text)
    return [word_tokenize(sentence) for sentence in sentences]

data['tokenized_text'] = data['text'].apply(preprocess)
```

Step 3: Extractive Summarization

Implement TextRank for extractive summarization:
```
from gensim.summarization import summarize

def extractive_summary(text, ratio=0.2):
    return summarize(text, ratio=ratio)

data['extractive_summary'] = data['text'].apply(lambda x: extractive_summary(x))
```

Step 4: Abstractive Summarization

Use a pre-trained model for abstractive summarization:
```
from transformers import pipeline

summarizer = pipeline('summarization')

def abstractive_summary(text, max_len=50, min_len=10):
    return summarizer(text, max_length=max_len, min_length=min_len, do_sample=False)[0]['summary_text']

data['abstractive_summary'] = data['text'].apply(lambda x: abstractive_summary(x))
```

Step 5: Evaluation

Evaluate summarization quality using ROUGE scores:
```
from rouge import Rouge

rouge = Rouge()

def evaluate_summary(original, summary):
    scores = rouge.get_scores(summary, original)
    return scores

data['rouge_scores'] = data.apply(lambda row: evaluate_summary(row['text'], row['abstractive_summary']), axis=1)
```

5. Expected Outcomes

1. A functional tool capable of generating extractive and abstractive summaries.
2. Summaries that capture the essence of the original text with high accuracy.
3. Insights into the performance and suitability of different summarization techniques.

6. Additional Suggestions

- Experiment with advanced transformer models like Pegasus for abstractive summarization.
- Integrate the summarization tool into a web application using Flask or Django.
- Incorporate multi-document summarization for better usability.