Project Overview
This project focuses on **abstractive text summarization**, a more advanced technique than simple extractive summarization. It involves building a model that can read a large body of text and create a new, original summary that captures the main ideas without simply copying and pasting sentences from the source document. This demonstrates a deep understanding of natural language processing and generative modeling.
Tech Stack & Tools
- Python: The primary programming language for development.
- Hugging Face Transformers: Used to access and fine-tune pre-trained models like T5 or BART.
- PyTorch/TensorFlow: The deep learning frameworks used for model training.
- Datasets: Used to handle and prepare large text corpora for training.
My Process
Phase 1: Model Selection & Data Preparation
I began by selecting an appropriate pre-trained transformer model known for summarization tasks. I then acquired and prepared a large text dataset, splitting it into articles and their corresponding human-written summaries for fine-tuning.
Phase 2: Fine-Tuning the Model
The chosen model was fine-tuned on the summarization dataset. This process involved training the model to learn the specific patterns required to generate concise and accurate summaries from the provided text.
Phase 3: Integration & Evaluation
After fine-tuning, I built a simple interface (or API) to allow users to input text and receive a summary. I then evaluated the model's performance using metrics like ROUGE score to measure the quality of the generated summaries.
Results & Future Work
The resulting summarizer can accurately condense complex articles into readable, concise summaries. This project successfully demonstrates my ability to work with large language models and apply them to real-world problems.
Future Enhancements:
- Optimize the model for real-time summarization on a larger scale.
- Add functionality for summarizing different types of content, such as audio transcripts or meeting notes.
- Integrate a feature that allows the user to specify the desired summary length.