Text summarization refers to the technique of shortening long pieces of text. The intention is to create a coherent and fluent summary having only the main points outlined in the document. Automatic text summarization is a common problem in machine learning and natural language processing (NLP).Text summarization is the problem of creating a short, accurate, and fluent summary of a longer text document. Automatic text summarization methods are greatly needed to address the ever-growing amount of text data available online to both better help discover relevant information and to consume relevant information faster.
There are two main techniques in summarization.
1 - Extractive text summarization
2 - Abstractive text summarization
Extractive text summarization methods function by identifying the important sentences or excerpts from the text and reproducing them verbatim as part of the summary. No new text is generated; only existing text is used in the summarization process.
Abstractive text summarization methods employ more powerful natural language processing techniques to interpret text and generate new summary text, as opposed to selecting the most representative existing excerpts to perform the summarization.
Here I am explaining six different approaches for text summarization. Those are
1 - Natural Language Toolkit(NLTK)
2 - Bidirectional Encoder Representations from Transformers(BERT)
3 - Transformer pipeline
4 - BART(BERT + GPT)
5 - Text-To-Text Transfer Transformer(T5)
6 - AdaptNLP
7 - PEGASUS