Code Pattern

Text summarization and visualization using IBM Watson Studio

Use extractive summarization to help reduce reading time, make the selection process easier, and improve the effectiveness of indexing

Automatic text summarization is part of natural language processing by which computers can understand, derive meaning and analyze human language. Text summaries can help reduce reading time, make the selection process easier, and improve the effectiveness of indexing. Text summarization algorithms are also less biased than human summarizers. Personalized summaries are useful in question-answering systems because they provide personalized information. Using automatic or semi-automatic summarization systems enable commercial abstract services to increase the number of texts they’re able to process.

More:  https://developer.ibm.com/patterns/text-summarization-topic-modelling-using-watson-studio-watson-nlu