Vol. 8, Issue 1 (2019)

Author(s):
Meghna Chaudhary

Abstract:
This study introduces a Python-based text summarizer that mines a text document for key information using natural language processing (NLP) methods. Extractive summarization is implemented by the text summarizer using TextBlob and NLTK, two well-known NLP packages. In contrast to TextBlob, which uses its own extractive summarization solution, NLTK uses the TextRank algorithm and Latent Semantic Analysis (LSA) for summarization. A dataset of news stories is used to test the text summarizer's performance, and the results demonstrate its capacity to provide precise and succinct summaries. Also, the benefits and drawbacks of NLTK and TextBlob are examined, giving information on their usefulness and suitability for text-summarizing jobs. This Python-based text summarizer could be used in a number of different fields, such as news article summarization, legal document summarization, and product review summarization.