Auto-Enhancement of Text Summarization by using Extractive Method
Text summarization, Extractive method, Natural language processing(NLP),Auto- Enhancement, text.
The purpose of this undertaking of auto-enhancement of text summarization the usage of extractive methods in NLP entails automatically enhancing the fine of summaries generated by means of extracting the most important sentences or phrases from a textual content. Extractive summarization involves identifying and extracting the most applicable sentences or phrases from a larger textual content, in place of producing new text. NLP techniques consisting of Named Entity reputation (NER), part-of-Speech (POS) tagging, and semantic analysis can be used to identify critical sentences and phrases based totally on their relevance to the overall that means of the textual content. To beautify the satisfactory of an extractive summary, one approach is to use system mastering algorithms to discover the maximum important capabilities of the textual content, together with keywords, named entities, and sentiment. those features can then be used to weight the importance of person sentences or phrases, ensuring that the precis captures the most vital records in the authentic textual content. average, auto-enhancement of text summarization the use of extractive techniques in NLP has the capability to noticeably enhance the efficiency and accuracy of summarization responsibilities, particularly in fields which include information media and educational studies wherein summarization is regularly required to quick and correctly bring essential facts.
Article Details
Unique Paper ID: 159027

Publication Volume & Issue: Volume 9, Issue 11

Page(s): 151 - 159
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