This project was for the course CS-286: Natural Language Processing.

Automatic Text Summarization Techniques

This project focuses on implementing automatic text summarization using two different methods: extractive and abstractive summarization.

Extractive Summarization

The extractive method in this project utilizes the TextRank algorithm.

Abstractive Summarization

For abstractive summarization, three transformer models were employed, namely BERT2BERT pre-trained, BERT2BERT fine-tuned, and T5 fine-tuned. These models were fine-tuned using the CNN-DailyMail dataset available on Kaggle.

Interactive GUI

An interactive GUI was created using Anvil app and Google Colab, providing a user-friendly interface for text summarization.

Report

A report in IEEE format, containing all the information related to this project, is available in the report.pdf file.

Code Files

The code files related to fine-tuning the models can be found in the following locations:

• finetuning_bert_models.py
• finetuning_t5_model.py

The code for the TextRank algorithm as well as for creating the Anvil GUI is located in the file summarization_gui.py.