Differences Between Extractive And Non-Extractive Text Summarization
Text summarization is becoming increasingly important as the volume of written materials increases exponentially. It allows individuals to quickly and easily assess large amounts of written data and extract key points from it so that they can be used in a time-efficient manner. While there are many different techniques available for text summarization, two of the most common techniques are extractive and non-extractive summarization.
At the most basic level, extractive text summarization involves selecting key sentences and phrases from the original text and combining them into a condensed version of the text. For example, if given an article about a particular topic, an extractive summarization tool might select the most important sentences from the article and combine them into a single, condensed version of the text. Extractive summarization tools usually employ Natural Language Processing tools to extrapolate the most relevant phrases from the original text.
Non-extractive summarization, on the other hand, uses Natural Language Processing algorithms and Machine Learning algorithms to generate completely new summaries of the given text. These summaries are often generated by analyzing the structure and content of the text, as well as any of the specific words or phrases used in the text. Non-extractive summarization does not rely on selecting existing sentences or phrases from the original text.
One of the biggest distinctions between extractive and non-extractive summarization is the degree of accuracy. Extractive summarization tools are great for quickly condensing large amounts of text, but they often lack accuracy due to the nature of the selection process. Non-extractive summarization tools, however, can generate summaries that are more accurate and concise due to the use of machine learning algorithms.
Another difference between the two techniques is the time it takes to generate the summaries. Extractive summarization is often faster, as it only requires selecting the most important sentences or phrases from the text and inserting them into the condensed version. Non-extractive summarization, on the other hand, requires more time, as it requires more analysis of the text in order to generate a new, more accurate summary.
In conclusion, extractive and non-extractive text summarization are two different techniques that can be used to quickly and easily summarize large amounts of text. Extractive summarization is great for quickly condensing large amounts of text, but it lacks accuracy due to the nature of the selection process. Non-extractive summarization, on the other hand, generates more accurate summaries due to the use of machine learning algorithms, but it requires more time to generate the summaries.
