Summarization
Summarization
To summarize the information for quicker consumption in large texts is called Summarization. When you visit a site, you do not generally start reading every article; instead, you glance through short and informative summaries. To help you do so, Summarization is the process that extracts these summaries out. Here we will discuss advanced and traditional Summarization methods used for Text Summarization for NLP.
Summarization
To summarize the information for quicker consumption in large texts is called Summarization. When you visit a site, you do not generally start reading every article; instead, you glance through short and informative summaries. To help you do so, Summarization is the process that extracts these summaries out. Here we will discuss advanced and traditional Summarization methods used for Text Summarization for NLP.
Types of Summarization
Summarization can be grouped into two main types: Extractive Text Summarization and Abstractive Text Summarization.
Extractive Text Summarization
This was the first traditional method of summarization developed. The key goal is to identify the important context of the text and shape them into a summary. It should be noted that a summary contains exact meaning from the original text and shouldn’t be omitted during the process.
Abstractive Text Summarization
This is a rather advanced method of summarization. With this approach, we identify significant sections, take the context and reproduce them in a new manner. This aims to achieve vital information through the shortest possible text. In this method of summarization, sentences are generated rather than extracted from the said text.
Both types can be used in relation to various NLP techniques. Having explained the types, let’s take a brief look into the algorithms used for summarization.
Summarization using Algorithms
Summarization NLP can be achieved using numerous algorithms such as TextRank, Sumy, Lex Rank, LSA (Latent Semantic Analysis), and so on. TextRank is based on the idea that the words that occur repetitively are significant and so TextRank is designed with respect to Python to function accordingly. Sumy allows you to import your wanted algorithm instead of coding on your own. LexRank ensures that a specific sentence is suggested by other sentences and therefore is ranked higher. LSA (Latent Semantic Analysis) is an unmonitored algorithm model that can be used for extractive text summaries.
Summarization NLP Python is pretty much the future and it is a very reliable process at that, especially when you take the aforementioned algorithms into consideration. As suggested by NLP Australia, on the subject matter of Text Summarization, make sure not to miss out on those on the subject of summarization.