NLP: Text Extraction: Summarization

KEEP IN TOUCH

Text Summarization is about creating a concise summary of a longer text to capture its main ideas. It can be likened to crafting brief notes for efficient review before an exam, condensing comprehensive information into a succinct format. In essence, it generates an accurate summary of a more extensive text.

TYPES

Text Summarization can be categorized into two main types:

1. Extractive Summarization: Extractive Summarization involves selecting and extracting the most vital sentences or phrases directly from the source text. The selection is based on relevance and cohesiveness in representing the key points of the original text. Examples include summarizing news articles, legal documents, and research papers.
2. Abstractive Summarization: Abstractive Summarization goes beyond extraction by generating novel sentences not present in the original text. This process requires a deeper analysis of the content and can be applied to create concise summaries of medical reports, business documents, social media posts, and user-generated content.

STEPS:

1. Text Preprocessing: This step involves data cleaning, including lowercasing, removing special characters, and eliminating stop words.