Project-7: Text summarization

Overview

Welcome to our Text Summarization Project! This Readme provides a comprehensive understanding of our project’s purpose, goals, requirements, methodology, architecture, pipeline, and conclusion.

1. Model Overview

Our project focuses on creating a text summarization model using the Samsum dataset. The main objective is to automate the process of summarizing lengthy pieces of text while retaining their key information and meaning. Leveraging machine learning techniques, our model aims to produce concise and coherent summaries.

2. Motivation

The proliferation of information makes efficient content comprehension vital. Our model addresses this need by providing automated text summarization. This technology has applications in information retrieval, content curation, and enhancing productivity.

3. Success Metrics

Our model’s success will be gauged through the following metrics:

  • ROUGE Scores: To evaluate the quality of summaries generated by comparing them with reference summaries.
  • Processing Speed: Measuring the time taken to generate summaries for input text.
  • User Feedback: Collecting input from users to assess the relevance and accuracy of the generated summaries.

4. Requirements & Constraints

4.1. Functional Requirements

  • Develop a versatile model capable of summarizing various types of input text.
  • Implement a user-friendly API to accept text inputs and provide generated summaries.

4.2. Non-Functional Requirements

  • Accuracy: The generated summaries should accurately reflect the core content of the input text.
  • Scalability: The model should handle a substantial number of summarization requests.
  • Usability: The API should be easy to integrate and use.

4.3. Constraints

  • Limited computational resources for model training and deployment.
  • Summarization process should complete within a reasonable timeframe.

4.4. Out-of-Scope

  • Translation of languages not covered by the Samsum dataset.
  • Handling of domain-specific texts requiring specialized models.

5. Methodology

5.1. Problem Statement

Our objective is to create a machine learning model that can take longer texts and generate concise, meaningful summaries.

5.2. Data

We will utilize the Samsum dataset, which offers a diverse range of texts and their corresponding summaries.

5.3. Techniques

We’ll explore both extractive (selecting existing sentences) and abstractive (generating new sentences) summarization techniques.

6. Architecture

Our architecture includes:

  • Data Preprocessing: Cleaning and tokenizing input text data.
  • Model Training: Training extractive and abstractive summarization models.
  • UI Development: Creating an UI to facilitate user interaction.
  • Model Evaluation: Assessing summary quality using ROUGE scores and user input.

7. Pipeline

  1. Collect and preprocess the Samsum dataset.
  2. Train extractive and abstractive summarization models.
  3. Develop an UI for user requests.
  4. Evaluate the model’s performance using metrics and feedback.
  5. Refine the model based on results and user input.

8. Conclusion

Our Text Summarization Project aims to automate content condensation, enhancing comprehension and efficiency. By using machine learning, we strive to offer accurate and relevant summaries. Through continuous evaluation and user collaboration, our goal is to refine the model’s capabilities.

More about the Project: GitHub Repo