NLP Tutorial 12 – Text Summarization using NLP
Text summarization is the process of creating a short, accurate, and fluent summary of a longer text document. It is the process of distilling the most important information from a source text. Automatic text summarization is a common problem in machine learning and natural language processing (NLP). Automatic text summarization methods are greatly needed to address the ever-growing amount of text data available online to both better help discover relevant information and to consume relevant information faster.
An Extractive summarization method consists of selecting important sentences, paragraphs, etc. from the original document and concatenating them into a shorter form. Abstractive summarization is an understanding of the main concepts in a document and then expressing those concepts in clear natural language. The Domain-specific summarization techniques utilize the available knowledge specific to the domain of the text. For example, automatic summarization research on medical text generally attempts to utilize the various sources of codified medical knowledge and ontologies. The Generic summarization focuses on obtaining a generic summary or abstract of the collection of documents, or sets of images, or videos, news stories, etc. query-based summarization, sometimes called query-relevant summarization, summarizes objects specific to a query. The Multi-document summarization is an automatic procedure aimed at the extraction of information from multiple texts written about the same topic. The resulting summary report allows individual users, such as professional information consumers, to quickly familiarize themselves with the information contained in a large cluster of documents. The Single-document summarization generates a summary from a single source document.
🔊 Watch till last for a detailed description
01:21 What is text summarization?
05:19 Installing the packages
15:10 Sentence tokenization
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