https://github.com/mohankrishnagr/infosys_springboard_text-summarization
GROUP 4. This repository contains the implementation of a Transformer-based model for abstractive text summarization and a rule-based approach for extractive text summarization.
https://github.com/mohankrishnagr/infosys_springboard_text-summarization
automatic-summarization bart datasets deep-learning kmeans-clustering nlp pytorch tf-idf trainer transformer
Last synced: 8 months ago
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GROUP 4. This repository contains the implementation of a Transformer-based model for abstractive text summarization and a rule-based approach for extractive text summarization.
- Host: GitHub
- URL: https://github.com/mohankrishnagr/infosys_springboard_text-summarization
- Owner: MohanKrishnaGR
- Created: 2024-06-28T14:13:44.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-17T14:58:26.000Z (about 1 year ago)
- Last Synced: 2024-07-20T00:53:47.621Z (about 1 year ago)
- Topics: automatic-summarization, bart, datasets, deep-learning, kmeans-clustering, nlp, pytorch, tf-idf, trainer, transformer
- Language: Jupyter Notebook
- Homepage: http://text-summarizer.bqegenbyedfzhpa3.centralindia.azurecontainer.io:8000/
- Size: 9.51 MB
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
[](https://github.com/MohanKrishnaGR/Infosys_Springboard_Text-Summarization/actions/workflows/azure.yml)
# Text Summarization
A project by AI/ML Interns (Group 4) @ Infosys Springboard, Summer 2024.
## Mentor
Mr. Narendra Kumar
## Contents
- [Problem Statement](#problem-statement)
- [Project Statement](#project-statement)
- [Approach to Solution](#approach-to-solution)
- [Background Research](#background-research)
- [Solution](#solution)
- [Workflow](#workflow)
- [Data Collection](#data-collection)
- [Abstractive Text Summarization](#abstractive-text-summarization)
- [Extractive Text Summarization](#extractive-text-summarization)
- [Testing](#testing)
- [Deployment](#deployment)
- [Containerization](#containerization)
- [CI/CD Pipeline](#cicd-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](https://hub.docker.com/layers/mohankrishnagr/infosys_text-summarization/group/images/sha256-28802ba2a3b30d36b94fbd878c97585c02c813534fc80fdca5e81494b96bfd08?context=explore)
## 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`
- **IPv4 Address:** [Text Summarizer](http://20.235.235.107:8000/) ( http://20.235.235.107:8000/ )
- **FQDN:** [Text Summarizer](http://text-summarizer.bqegenbyedfzhpa3.centralindia.azurecontainer.io:8000/) ( http://text-summarizer.bqegenbyedfzhpa3.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.