Implementation of NLP based automatic text summarization using spacy
Keywords:
Empirical Methods, Text Summarization, Extraction, Abstraction, Reinforcement Learning, Supervised, Unsupervised, NLP, Spacy AlgorithmsAbstract
The amount of data on the Internet has increased exponentially over the past decade. Therefore, we need a solution that converts this massive amount of raw information into useful information that the human brain can understand. One such common technique in research that helps when dealing with large amounts of data is text summarization. Automatic summarization is a well-known approach to reduce documents to key ideas. This works by storing important information by creating a shortened version of the text. Text summaries are divided into extraction and abstraction methods. The extraction summary method minimizes the summarization burden by selecting a subset of relevant sentences from the actual text. There are many methods, but researchers specializing in natural language processing (NLP) are particularly attracted to the extraction method. The meaning of the sentence is calculated using linguistic and statistical features. In this work, extractive and abstract methods for summarizing texts were examined. This white paper uses a spacey algorithm to analyze the above methods, resulting in fewer iterations and a more focused summary.
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