Let’s create a project for automated document summarization using Python and the Gensim library. In this example, we’ll use the TextRank algorithm for extractive summarization.
1. Project Setup:
- Create a new Python project or script.
- Install necessary libraries:
pip install gensim
2. Document Preprocessing:
- Load and preprocess a sample document for summarization:
from gensim.summarization import summarize
# Sample document for summarization
document = """
Gensim is an open-source library for unsupervised topic modeling and natural language processing.
It uses scalable statistical models to analyze and summarize large text corpora.
The library is designed to process raw, unstructured digital texts and extract meaningful insights.
Gensim's TextRank algorithm is commonly used for extractive summarization.
This project demonstrates how to use Gensim for automated document summarization.
"""
# Perform extractive summarization using TextRank
summary = summarize(document)
print("Original Document:")
print(document)
print("\nSummarized Document:")
print(summary)
3. User Interaction:
- Allow the user to input a document and generate a summary:
def generate_summary(user_input):
summary = summarize(user_input)
print("\nGenerated Summary:")
print(summary)
# User interaction loop
while True:
user_input = input("\nEnter a document for summarization (or 'exit' to end):\n")
# Exit the loop if the user types 'exit'
if user_input.lower() == 'exit':
break
generate_summary(user_input)
4. Project Conclusion:
- Summarize the project’s goals, outcomes, and potential improvements.
- Include any insights gained from summarizing documents using the Gensim library.
This project provides a simple example of automated document summarization using Gensim’s TextRank algorithm. You can explore more advanced summarization techniques, customize parameters, and integrate with larger datasets for improved summarization results. Additionally, you may want to experiment with other libraries and algorithms for abstractive summarization.