Implementasi Algoritma Graf dan Algoritma Genetika pada Peringkasan Single Document

Repositor ◽  
2020 ◽  
Vol 2 (11) ◽  
pp. 1521
Author(s):  
Lina Dwi Yulianti ◽  
Setio Basuki ◽  
Yufis Azhar
Keyword(s):  
Genetic Algorithms ◽  
Graph Algorithms ◽  
System Development ◽  
High Accuracy ◽  
Text Summarization ◽  
Test Results ◽  
The Core ◽  
Automatic Text Summarization ◽  
Summarization System ◽  
Automatic Text

In today's technological advancements, finding information is easier and faster. But not a little information that is not true or commonly referred to as hoaxes. Therefore, information must be obtained from several sources to ensure the accuracy of the information. Automatic Text Summarization System is a system used for text based document summarization. This system can help find the core of a news document, so it does not require much time to read. Researchers use Graph Algorithms and Genetic Algorithms in system development. From the test results obtained by the accuracy of the system produced by the system with manual numbers have a cosine similarity value of 71.21%. This can prove that the system built can be used by users because the results of tests carried out get high accuracy values.

scholarly journals Automatic Text Summarization of COVID-19 Research Articles Using Recurrent Neural Networks and Coreference Resolution

2021 ◽  
Author(s):  
Mahsa Afsharizadeh ◽  
Hossein Ebrahimpour-Komleh ◽  
Ayoub Bagheri
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Text Summarization ◽  
Medical Community ◽  
Research Articles ◽  
Coreference Resolution ◽  
Extractive Summarization ◽  
Automatic Text Summarization ◽  
Summarization System ◽  
Automatic Text

Purpose: Pandemic COVID-19 has created an emergency for the medical community. Researchers require extensive study of scientific literature in order to discover drugs and vaccines. In this situation where every minute is valuable to save the lives of hundreds of people, a quick understanding of scientific articles will help the medical community. Automatic text summarization makes this possible. Materials and Methods: In this study, a recurrent neural network-based extractive summarization is proposed. The extractive method identifies the informative parts of the text. Recurrent neural network is very powerful for analyzing sequences such as text. The proposed method has three phases: sentence encoding, sentence ranking, and summary generation. To improve the performance of the summarization system, a coreference resolution procedure is used. Coreference resolution identifies the mentions in the text that refer to the same entity in the real world. This procedure helps to summarization process by discovering the central subject of the text. Results: The proposed method is evaluated on the COVID-19 research articles extracted from the CORD-19 dataset. The results show that the combination of using recurrent neural network and coreference resolution embedding vectors improves the performance of the summarization system. The Proposed method by achieving the value of ROUGE1-recall 0.53 demonstrates the improvement of summarization performance by using coreference resolution embedding vectors in the RNN-based summarization system. Conclusion: In this study, coreference information is stored in the form of coreference embedding vectors. Jointly use of recurrent neural network and coreference resolution results in an efficient summarization system.

scholarly journals Automatic Text Summarization by Providing Coverage, Non-Redundancy, and Novelty Using Sentence Graph

2022 ◽  
Vol 15 (1) ◽  
pp. 1-18
Author(s):  
Krishnaveni P. ◽  
Balasundaram S. R.
Keyword(s):  
Graph Algorithms ◽  
Maximal Clique ◽  
Text Summarization ◽  
Original Text ◽  
Online Information ◽  
Automatic Text Summarization ◽  
Global Properties ◽  
Input Text ◽  
Local Properties ◽  
Automatic Text

The day-to-day growth of online information necessitates intensive research in automatic text summarization (ATS). The ATS software produces summary text by extracting important information from the original text. With the help of summaries, users can easily read and understand the documents of interest. Most of the approaches for ATS used only local properties of text. Moreover, the numerous properties make the sentence selection difficult and complicated. So this article uses a graph based summarization to utilize structural and global properties of text. It introduces maximal clique based sentence selection (MCBSS) algorithm to select important and non-redundant sentences that cover all concepts of the input text for summary. The MCBSS algorithm finds novel information using maximal cliques (MCs). The experimental results of recall oriented understudy for gisting evaluation (ROUGE) on Timeline dataset show that the proposed work outperforms the existing graph algorithms Bushy Path (BP), Aggregate Similarity (AS), and TextRank (TR).

scholarly journals A Multi-document Summarization System for News Articles in Portuguese using Integer Linear Programming

2019 ◽  
Author(s):  
Laerth Gomes ◽  
Hilário Oliveira
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Relevant Information ◽  
Brazilian Portuguese ◽  
Text Summarization ◽  
Document Summarization ◽  
Automatic Text Summarization ◽  
Summarization System ◽  
Automatic Text ◽  
Intense Research

Automatic Text Summarization (ATS) has been demanding intense research in recent years. Its importance is given the fact that ATS systems can aid in the processing of large amounts of textual documents. The ATS task aims to create a summary of one or more documents by extracting their most relevant information. Despite the existence of several works, researches involving the development of ATS systems for documents written in Brazilian Portuguese are still a few. In this paper, we propose a multi-document summarization system following a concept-based approach using Integer Linear Programming for the generation of summaries from news articles written in Portuguese. Experiments using the CSTNews corpus were performed to evaluate different aspects of the proposed system. The experimental results obtained regarding the ROUGE measures demonstrate that the developed system presents encourage results, outperforming other works of the literature.

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