Text summarization is the process of condensing a large text document into a shorter summary while retaining the essential information. This task is critical for extracting the most important information from large volumes of text, such as news articles, academic papers, and legal documents. Machine learning algorithms, such as word frequency-based summarization, can provide an efficient and scalable solution to this problem.
In this project, we aim to use machine learning algorithms to summarize large text documents using word frequency-based techniques. We will use a range of features, such as word frequency, sentence length, and paragraph structure, to train the machine learning models. The proposed workflow for the Text Summarization project includes the following steps:
- Data Collection and Preprocessing: We will collect a dataset of text documents, such as news articles or academic papers, and preprocess the dataset by cleaning and normalizing the text, removing stop words, and performing feature extraction.
- Feature Extraction: We will extract a set of features from the text documents, such as word frequency, sentence length, and paragraph structure. We will also engineer new features, such as the presence of specific keywords or phrases, to improve the model’s performance.
- Model Training and Selection: We will train a set of machine learning models, such as linear regression, decision trees, and neural networks, on the preprocessed dataset. We will evaluate the performance of each model using metrics such as the Rouge score, which measures the similarity between the generated summary and the reference summary, and select the best-performing model.
- Model Evaluation and Deployment: We will evaluate the performance of the selected model using cross-validation and backtesting techniques. We will then deploy the model to a cloud-based platform or mobile app, which can automatically generate summaries of new text documents in real-time.
The expected outcomes of this project include a scalable and efficient machine learning algorithm for text summarization using word frequency-based techniques, a comprehensive dataset of text documents, and a set of best practices and guidelines for applying machine learning algorithms to text summarization. The project has numerous applications, including news article summarization, academic paper summarization, and legal document summarization. The insights gained from this project can also inform decision-making in other domains, such as social media analysis and customer feedback analysis.
