This repository contains the implementation of an extractive text summarization model for Urdu articles. The project focuses on generating concise summaries for Urdu language articles using a data-driven approach.

The objective of this project is to build a machine learning model that can extract important sentences from Urdu articles and generate a summary that captures the key information. Extractive summarization involves identifying the most relevant and informative sentences from the original text and combining them to form a coherent summary.

The dataset used for training and evaluation consists of a collection of Urdu language articles. The articles cover a wide range of topics and domains. Each article is associated with a human-generated summary that serves as the reference summary for evaluation purposes.

The dataset is divided into three subsets: training, validation, and testing. The training set is used to train the model, the validation set is used for hyperparameter tuning and model evaluation during training, and the testing set is used for final evaluation and performance assessment.

The extractive summarization model is implemented using advanced natural language processing techniques and machine learning algorithms. The architecture typically involves the following steps:

The Urdu text undergoes preprocessing steps such as tokenization, removing stop words, and handling punctuation and special characters. The text may also be normalized to handle variations in spellings or forms.

Various features are extracted from the preprocessed text to capture important information. These features may include word frequency, sentence position, sentence length, and other linguistic properties.

Each sentence in the text is assigned a score based on its relevance and importance. The scoring can be based on different criteria, such as the occurrence of important keywords, the similarity to the title or reference summary, or the overall informativeness.

The top-scoring sentences are selected and combined to form the summary. The selection can be based on a fixed number of sentences or a predetermined summary length.

The implementation of the Urdu article summarization model involves the following steps:

The Urdu articles are processed to remove noise, tokenize the text, remove stop words, and apply any other necessary preprocessing steps to prepare the data for training and evaluation.

Relevant features are extracted from the preprocessed text. These features can include word frequencies, sentence positions, or any other linguistic or contextual information that can assist in scoring the sentences.

The extracted features are used to train a machine learning model or build a rule-based system that assigns scores to the sentences. The model is trained using the training dataset and tuned using the validation dataset.

The trained model is applied to the test dataset to score each sentence in the Urdu articles. The sentences are ranked based on their scores.

The top-scoring sentences are selected and combined to generate the final summary. The selected sentences are concatenated in a coherent manner to form a concise summary that captures the main points of the original article.