Summarisation

Summarization in NLP

Summarization in natural language processing (NLP) is the process of condensing a text document into a shorter version while retaining its main ideas and key information. There are two primary forms of summarization:

The unsupervised summarization process involves splitting text, tokenizing sentences, assigning scores based on importance, and selecting top sentences. Effective scoring methods include calculating sentence similarity and analyzing word frequencies to ensure that the summary captures the essence of the original text.

Extraction:

• This method involves selecting specific words or sentences directly from the original text to create a summary. It focuses on identifying and pulling out the most important parts of the text without altering the original wording. Abstraction:
• The abstraction method generates a summary that may include new words and phrases not present in the original text. This approach is more complex as it requires understanding the content and rephrasing it, often using techniques like paraphrasing.

Unsupervised Summarization Process

The basic idea behind unsupervised summarization involves the following steps:

1. Split Text into Sentences: The text is divided into individual sentences for analysis.

2. Tokenize Sentences: Each sentence is tokenized into separate words, allowing for detailed examination of word usage.

3. Assign Scores to Sentences: Sentences are evaluated based on their importance, which is a crucial step in the summarization process.

4. Select Top Sentences: The highest-scoring sentences are selected and displayed in their original order to form the summary.

Methods for Assigning Scores

The main point of summarization is effectively assigning scores to sentences. Here are some common methods for doing this:

• Similarity Calculation: Calculate the similarity between each pair of sentences and select those that are most similar to the majority of sentences. This helps identify sentences that capture the central themes of the text.

• Word Frequencies: Analyze word frequencies to identify the most common words in the text. Sentences that contain a higher number of these frequent words are then selected for the summary.