A Deep Look into Extractive Text Summarization
A Deep Look into Extractive Text Summarization作者机构：Facultad de Informática Universidad Autónoma de Querétaro Querétaro México
出 版 物：《Journal of Computer and Communications》 (电脑和通信（英文）)
年 卷 期：2021年第9卷第6期
页 面：24-37页
学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学]
主 题：Text Mining Preprocesses Text Summarization Extractive Text Sumarization
摘 要：This investigation has presented an approach to Extractive Automatic Text Summarization (EATS). A framework focused on the summary of a single document has been developed, using the Tf-ldf method (Frequency Term, Inverse Document Frequency) as a reference, dividing the document into a subset of documents and generating value of each of the words contained in each document, those documents that show Tf-Idf equal or higher than the threshold are those that represent greater importance, therefore;can be weighted and generate a text summary according to the user’s request. This document represents a derived model of text mining application in today’s world. We demonstrate the way of performing the summarization. Random values were used to check its performance. The experimented results show a satisfactory and understandable summary and summaries were found to be able to run efficiently and quickly, showing which are the most important text sentences according to the threshold selected by the user.
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