Surfing the internet for a specific topic is a common act done these days. Having information on everything and anything is just right there. The information display by the search engine is not always what is expected. Then, we look through the little paragraphs below each links to find out its content, which normally summarizes the article. The interest has a large number of researches, news, article, blogs and webpages and it is not possible to make a summary of each article manually. New information is pumped into the internet every minute. Forming a concise summary from a long article is a common example, but there are a lot more of such write ups which are available and may be required also.
Google, Bing and yahoo search engines makes use of automatic text summarizing tools to make summaries for ling text documents. A summarizer is an algorithm which removes sentences from a text document, chooses which are relevant, and returns the sentences in a structured and readable way in shorter texts and automatic text summarization is partners the field of natural language processing, in which computers analyze and derive meaning from human language.
There are two major ways to summarizing text documents using Automatic Summarization tools; they included:
- Abstractive method
- Extractive method
Text summarization is divided based in its purpose like generic, query-based or domain specific, its input type like if it is single or multi document and its output type if extractive or abstract.
In extractive summarization, new summary is gotten from the original documents by selection of sentences and phrases. It employs a number of techniques ranking the importance of phrases so as to choose those which are most relevant to the meaning of the source.
In Abstract text summarization, completely new sentences and phrases are formed so as to describe the source document. It poses as a harder way of summarizing and provides results which are realistic because it is the method humans use most. The operation technique is by selection and compression contents from the original document but may also have words added to it.
Extractive summarization technique are used more commonly due to its easier use and availability, though abstractive methods are said to have more general solutions to the abstraction problem.