Think of it as a highlighter-which selects the main information from a source text. In extraction-based summarization, a subset of words that represent the most important points is pulled from a piece of text and combined to make a summary. The main types of text summarizationīroadly, there are two approaches to summarizing texts in NLP: extraction and abstraction. Implementing summarization can enhance the readability of documents, reduce the time spent in researching for information, and allow for more information to be fitted in a particular area.
Therefore, using automatic text summarizers capable of extracting useful information that leaves out inessential and insignificant data is becoming vital. For example, if you are looking for specific information from an online news article, you may have to dig through its content and spend a lot of time weeding out the unnecessary stuff before getting the information you want. However, most of this information is redundant, insignificant, and may not convey the intended meaning. Currently, we enjoy quick access to enormous amounts of information. With the present explosion of data circulating the digital space, which is mostly non-structured textual data, there is a need to develop automatic text summarization tools that allow people to get insights from them easily.
#Auto summarize machine for free
Ready to build, train, and deploy AI? Get started with FloydHub's collaborative AI platform for free Try FloydHub for free The need for text summarization An online news article that has been summarized using a text summarization machine learning algorithm
For example, the image below is of this news article that has been fed into a machine learning algorithm to generate a summary. Machine learning algorithms can be trained to comprehend documents and identify the sections that convey important facts and information before producing the required summarized texts. Text summarization is the technique for generating a concise and precise summary of voluminous texts while focusing on the sections that convey useful information, and without losing the overall meaning.Īutomatic text summarization aims to transform lengthy documents into shortened versions, something which could be difficult and costly to undertake if done manually. Automatic text summarization promises to overcome such difficulties and allow you to generate the key ideas in a piece of writing easily.
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