Effective Text Processing utilizing NLP
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Abstract
Summarizing is the practice of condensing a body of material into a more manageable size while retaining all of the key data elements and the intended meaning. Automatic text summarizing systems can now quickly retrieve summary phrases from input documents. However, it has a number of shortcomings, such as duplication, insufficient coverage, incorrect extraction of key lines, and poor sentence coherence. In this study, a new concept of summarizer technique is proposed using the Python spacy package. It extracts the most significant information from the text. The scoring system is also used to compute the score for the words in order to determine the word frequency. The findings show that the proposed method completes the summary process faster than the current algorithm. An online tool called the text to summary converter aids in material summarizing. This programmer will give us a summary of the data that we upload. The primary goal is to accurately summaries the data entered. The most crucial sentences will be removed before the unnecessary ones.
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