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by R K Sharath Kumar, Manjula Hosurmath | Published January 10, 2019
Artificial intelligenceData scienceMachine learningObject StoragePython
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.
In this pattern, we’ll demonstrate a methodology to summarize and visualize text using IBM Watson Studio. Text summarization is the process of creating a short and coherent version of a longer document. There are two methods to summarize text: extractive and abstractive summarization. We’ll focus on extractive summarization which involves the selection of phrases and sentences from the source document to make up the new summary. Techniques involve ranking the relevance of phrases in order to choose only those most relevant to the meaning of the source. We’ll also demonstrate different methods to visualize the data that can provide a quick view.
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