**Inverse Sentence Frequency (ISF)** is a measure designed to evaluate the importance of a word based on how frequently it appears **across sentences**. The underlying principle is that words appearing in many sentences are generally **less informative** regarding the specific content or themes of the text. Conversely, words that are present in fewer sentences are considered **more significant** as they likely pertain to more specific or unique aspects of the text. 

ISF quantifies this concept by assigning **higher scores** to words with **lower sentence distribution**, thereby highlighting their potential value in characterizing the text.

## Implementing ISF Calculation

The process of calculating ISF scores involves the following steps:

1. **Utilizing Word Distribution Counts**: The `word_sentence_counts` dictionary, prepared earlier, maps each word to the number of sentences it appears in. This data is essential for calculating ISF scores as it reflects the sentence-level distribution of words;

2. **Applying the ISF Formula**: For each word, the ISF score is calculated using a logarithmic scale. The formula `log(len(sentences) / word_sentence_counts[word])` takes the total number of sentences in the text and divides it by the count of sentences containing the word.

This project focuses on the design and implementation of a robust text summarizer, built using Python. By harnessing the capabilities of Python’s Natural Language Toolkit (NLTK), participants will gain hands-on experience in processing and analyzing textual data. The project covers a range of NLP techniques essential for text summarization. Participants will develop skills in parsing text and extracting meaningful content, learning how to filter essential information from large volumes of text.

We will be leveraging the powerful Natural Language Toolkit which is instrumental in the processing and analysis of textual data.

Extracting Text Meaning using TF-IDF

ISF Score

Implementing ISF Calculation

Oplossing