Zhong, M., Liu, P., Chen, Y., Wang, D., Qiu, X. and Huang, X. (2020) Extractive Summarization as Text Matching. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, July 2020, 6197-6208.
https://doi.org/10.18653/v1/2020.acl-main.552

has been cited by the following article:

• TITLE: A Deep Look into Extractive Text Summarization

  AUTHORS: Jhonathan Quillo-Espino, Rosa María Romero-González, Ana-Marcela Herrera-Navarro

  KEYWORDS: Text Mining, Preprocesses, Text Summarization, Extractive Text Sumarization

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.6, June 9, 2021

  ABSTRACT: 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.