Infosy_Text-Summarization

A project by Mohan Krishna G R, AI/ML Intern @ Infosys Springboard, Summer 2024.

Contents

• Problem Statement
• Project Statement
• Approach to Solution
• Background Research
• Solution
• Workflow
• Data Collection
• Abstractive Text Summarization
• Extractive Text Summarization
• Testing
• Deployment
• Containerization
• CI/CD Pipeline

Problem Statement

• Developing an automated text summarization system that can accurately and efficiently condense large bodies of text into concise summaries is essential for enhancing business operations.
• This project aims to deploy NLP techniques to create a robust text summarization tool capable of handling various types of documents across different domains.
• The system should deliver high-quality summaries that retain the core information and contextual meaning of the original text.

Project Statement

• Text Summarization focuses on converting large bodies of text into a few sentences summing up the gist of the larger text.
• There is a wide variety of applications for text summarization including News Summary, Customer Reviews, Research Papers, etc.
• This project aims to understand the importance of text summarization and apply different techniques to fulfill the purpose.

Approach to Solution

• Figure: Intended Plan

Background Research

• Literature Review

Solution

• Selected Deep Learning Architecture

Workflow

• Workflow for Abstractive Text Summarizer:

• Workflow for Extractive Text Summarizer:

Data Collection

• Data Preprocessing & Pre-processing Implemented in src/data_preprocessing.
• Data collection from different sources:
  • CNN, Daily Mail: News
  • BillSum: Legal
  • ArXiv: Scientific
  • Dialoguesum: Conversations
• Data integration ensures robust and multi-objective data, including News articles, Legal Documents – Acts, Judgements, Scientific papers, and Conversations.
• Validated the data through Data Statistics and Exploratory Data Analysis (EDA) using Frequency Plotting for every data source.
• Data cleansing optimized for NLP tasks: removed null records, lowercasing, punctuation removal, stop words removal, and lemmatization.
• Data splitting using sci-kit learn for training, testing, and validating the model, saved in CSV format.

Abstractive Text Summarization

Model Training & Evaluation

• Training:
  • Selected transformer architecture for ABSTRACTIVE SUMMARIZATION: fine-tuning a pre-trained model.
  • Chosen Facebook’s Bart Large model for its performance metrics and efficient trainable parameters.
    • 406,291,456 training parameters.

• Methods:
  • Native PyTorch Implementation
  • Trainer API Implementation

Method 1 - Native PyTorch

• Trained the model using manual training loop and evaluation loop in PyTorch. Implemented in: src/model.ipynb
• Model Evaluation: Source code:src/evaluation.ipynb
  • Obtained inconsistent results in inferencing.
  • ROUGE1 (F-Measure) = 00.018
  • There's a suspected tensor error while training using method 1, which could be attributed to the inconsistency of the model's output.
  • Rejected for the further deployment.
  • Dire need to implement alternative approach.

Method 2 – Trainer Class Implementation

• Utilized Trainer API from Hugging Face for optimized transformer model training. Implemented in: src/bart.ipynb

  • The model was trained with whole dataset for 10 epochs for 26:24:22 (HH:MM:SS) in 125420 steps.

• Evaluation: Performance metrics using ROUGE scores. Source code: src/rouge.ipynb

  • Model 2 - results outperformed that of method 1.
  • ROUGE1 (F-Measure) = 61.32 -> Benchmark grade
    • Significantly higher than typical scores reported for state-of-the-art models on common datasets.
  • GPT4 performance for text summarization - ROUGE1 (F-Measure) is 63.22
  • Selected for further deployment.

• Comparative analysis showed significant improvement in performance after fine-tuning. Source code: src/compare.ipynb

Extractive Text Summarization

• Rather than choosing computationally intensive deep-learning models, utilizing a rule based approach will result in optimal solution. Utilized a new-and-novel approach of combining the matrix obtained from TF-IDF and KMeans Clustering methodology.
• It is the expanded topic modeling specifically to be applied to multiple lower-level specialized entities (i.e., groups) embedded in a single document. It operates at the individual document and cluster level.
• The sentence closest to the centroid (based on Euclidean distance) is selected as the representative sentence for that cluster.
• Implementation: Preprocess text, extract features using TF-IDF, and summarize by selecting representative sentences.
  • Source code for implentation & evaluation: src/Extractive_Summarization.ipynb
  • ROUGE1 (F-Measure) = 24.71

Testing

• Implemented text summarization application using Gradio library for a web-based interface, for testing the model's inference.
• Source Code: src/interface.ipynb

Deployment

Application

• File Structure: summarize/

API Endpoints

• Developed using FastAPI framework for handling URLs, files, and direct text input.
  • Source Code: summarizer/app.py
• Endpoints:
  • Root Endpoint
  • Summarize URL
  • Summarize File
  • Summarize Text

Extractor Modules

• Extract text from various sources (URLs, PDF, DOCX) using BeautifulSoup and fitz.
• Source Code: summarizer/extractors.py

Extractive Summary Script

• Implemented extractive summarizer module. Same as implemented in: src/bart.ipynb
• Source Code: summarizer/extractive_summary.py

User Interface

• Developed a user-friendly interface using HTML, CSS, and JavaScript.
• Source Code: summarizer/templates/index.html

Containerization

• Developed a Dockerfile to build a Docker image for the FastAPI application.
• Source Code: summarizer/Dockerfile
• Image: Docker Image

CI/CD Pipeline

• Developed a CI/CD pipeline using Docker, Azure and GitHub Actions.
• Utilized Azure Container Instance (ACI) for deploying the image, triggers for every push to the main branch.
• Source Code: .github/workflows/azure.yml
  • .github/workflows/main.yml (AWS)
  • .github/workflows/azure.yml (Azure)
• To use the docker image run:

docker pull mohankrishnagr/infosys_text-summarization:final
docker run -p 8000:8000 mohankrishnagr/infosys_text-summarization:final

Then checkout at,

http://localhost:8000/

Deployed in AWS EC2 (Not Recommended under free trail)

Public IPv4:

http://54.168.82.95/

Deployed in Azure Container Instance (Recommended)

Public IPv4:

http://20.219.203.134:8000/

FQDN

http://mohankrishnagr.centralindia.azurecontainer.io:8000/

• Screenshots:

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End Note

Thank you for your interest in our project! We welcome any feedback. Feel free to reach out to us.