Project 4 (Text Summarization)

Creating an Extractive Text Summarizer

Description: 

In this project activity, you will create a basic extractive text summarizer using the TextRank algorithm. The goal is to extract the most important sentences from a given text and generate a summary that captures the main ideas. You will implement the necessary steps and evaluate the performance of the summarizer using an evaluation metric.

Steps:

1. Preprocessing: Load a text document or paragraph that you want to summarize. Perform basic preprocessing steps, such as converting the text to lowercase, removing punctuation, and tokenizing the text into sentences using a suitable library like NLTK.

2. Sentence Scoring: For each sentence in the preprocessed text, calculate a score based on factors such as word frequency, sentence length, and semantic similarity with other sentences. You can assign scores using heuristics or employ more advanced techniques like TF-IDF or word embeddings to calculate sentence similarity.

3. Select Top Sentences: Select the top-scoring sentences based on the scores calculated in the previous step. The number of sentences chosen depends on the desired length of the summary. You can experiment with different thresholds or select a fixed number of sentences.

4. Generate Summary: Combine the selected sentences to create a summary of the text. Ensure that the order of the sentences is maintained to preserve the coherence and flow of the original text. Join the sentences together, add appropriate punctuation, and display the final summary.

5. Evaluation: Evaluate the performance of your extractive summarizer using an evaluation metric such as ROUGE or F1 score. To do this, you will need a reference summary that captures the main ideas of the original text. Compare the generated summary to the reference summary and calculate the chosen evaluation metric. Note that you may need human-generated reference summaries for evaluation, especially if you want to use ROUGE or other metrics that rely on reference summaries.

Explanation:

Text summarization is a valuable technique for condensing large amounts of text into concise and informative summaries. In this project activity, you will focus on extractive summarization using the TextRank algorithm. The process involves preprocessing the text, scoring sentences based on certain criteria, selecting the top sentences, and generating a summary.

By implementing this activity, you will gain hands-on experience with the key steps involved in extractive text summarization. You will also explore the challenges associated with extracting important information from text and learn how to evaluate the performance of your summarizer.