Text summarization is one of famous NLP application which had been researched a lot and still at its nascent stage compared to manual summarization. In simple terms, the objective is to condense unstructured text of an article into a summary automatically. There are two types of summarization techniques.
For summarizing the reviews, we may want to pick highly positive and negative sentences as summary. Another approach could be picking only the sentences which contains some object/entity (Company name, person, dates, location etc). Also, if we want to summarize a report or an article, we may want to pick the important or say main sentences from the article.
Here, the problem of summarization converges to the problem “How do we know the main/important sentences (Text Rank algorithm)”. This problem is very similar to “How does google searches for important web pages from so many web pages having similar content” (Page Rank algorithm). It is an unsupervised approach.
Source: appliedmachinelearning.blog