scholarly journals Automatic Text Summarization Using Latent Drichlet Allocation (LDA) for Document Clustering

2015 ◽  
Vol 1 (3) ◽  
pp. 132 ◽  
Author(s):  
Erwin Yudi Hidayat ◽  
Fahri Firdausillah ◽  
Khafiizh Hastuti ◽  
Ika Novita Dewi ◽  
Azhari Azhari
Keyword(s):  
Clustering Algorithm ◽  
Document Clustering ◽  
Text Summarization ◽  
Data Set ◽  
Document Summarization ◽  
Automatic Text Summarization ◽  
Improve Accuracy ◽  
Automatic Document Summarization ◽  
Document Compression ◽  
Automatic Text

In this paper, we present Latent Drichlet Allocation in automatic text summarization to improve accuracy in document clustering. The experiments involving 398 data set from public blog article obtained by using python scrapy crawler and scraper. Several steps of clustering in this research are preprocessing, automatic document compression using feature method, automatic document compression using LDA, word weighting and clustering algorithm The results show that automatic document summarization with LDA reaches 72% in LDA 40%, compared to traditional k-means method which only reaches 66%.

A Quantum-Inspired Genetic Algorithm for Extractive Text Summarization

2021 ◽  
Vol 10 (2) ◽  
pp. 42-60
Author(s):  
Khadidja Chettah ◽  
Amer Draa
Keyword(s):  
Genetic Algorithm ◽  
State Of The Art ◽  
Text Summarization ◽  
Automated System ◽  
Evaluation Metrics ◽  
Document Summarization ◽  
Automatic Text Summarization ◽  
Reference Methods ◽  
Textual Data ◽  
Automatic Text

Automatic text summarization has recently become a key instrument for reducing the huge quantity of textual data. In this paper, the authors propose a quantum-inspired genetic algorithm (QGA) for extractive single-document summarization. The QGA is used inside a totally automated system as an optimizer to search for the best combination of sentences to be put in the final summary. The presented approach is compared with 11 reference methods including supervised and unsupervised summarization techniques. They have evaluated the performances of the proposed approach on the DUC 2001 and DUC 2002 datasets using the ROUGE-1 and ROUGE-2 evaluation metrics. The obtained results show that the proposal can compete with other state-of-the-art methods. It is ranked first out of 12, outperforming all other algorithms.

scholarly journals Survey of Scientific Document Summarization Techniques

2020 ◽  
Vol 21 (2) ◽  
Author(s):  
Sheena Kurian K ◽  
Sheena Mathew
Keyword(s):  
Text Summarization ◽  
Exponential Rate ◽  
Research Papers ◽  
Document Summarization ◽  
Automatic Text Summarization ◽  
Scientific Document Summarization ◽  
Pros And Cons ◽  
Comparison Of The Results ◽  
Evaluation Techniques ◽  
Automatic Text

The number of scientic or research papers published every year is growing at an exponential rate, which has led to an intensive research in scientic document summarization. The different methods commonly used in automatic text summarization are discussed in this paper with their pros and cons. Commonly used evaluation techniques and datasets in this field are also discussed. Rouge and Pyramid scores of the different methods are tabulated for easy comparison of the results.

scholarly journals Improved Text Summarization of News Articles Using GA-HC and PSO-HC

2021 ◽  
Vol 11 (22) ◽  
pp. 10511
Author(s):  
Muhammad Mohsin ◽  
Shazad Latif ◽  
Muhammad Haneef ◽  
Usman Tariq ◽  
Muhammad Attique Khan ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Text Summarization ◽  
Superior Performance ◽  
Text Documents ◽  
Automatic Text Summarization ◽  
News Websites ◽  
Adaptive Pso ◽  
Internet Availability ◽  
Automatic Text

Automatic Text Summarization (ATS) is gaining attention because a large volume of data is being generated at an exponential rate. Due to easy internet availability globally, a large amount of data is being generated from social networking websites, news websites and blog websites. Manual summarization is time consuming, and it is difficult to read and summarize a large amount of content. Automatic text summarization is the solution to deal with this problem. This study proposed two automatic text summarization models which are Genetic Algorithm with Hierarchical Clustering (GA-HC) and Particle Swarm Optimization with Hierarchical Clustering (PSO-HC). The proposed models use a word embedding model with Hierarchal Clustering Algorithm to group sentences conveying almost same meaning. Modified GA and adaptive PSO based sentence ranking models are proposed for text summary in news text documents. Simulations are conducted and compared with other understudied algorithms to evaluate the performance of proposed methodology. Simulations results validate the superior performance of the proposed methodology.

scholarly journals A Systematic Survey on Multi-document Text Summarization

2021 ◽  
Vol 10 (6) ◽  
pp. 3148-3153
Keyword(s):  
Deep Learning ◽  
Text Summarization ◽  
Evaluation Metrics ◽  
Automatic Process ◽  
Document Summarization ◽  
Text Document ◽  
Automatic Text Summarization ◽  
As Graph ◽  
Abstractive Summarization ◽  
Automatic Text

Automatic text summarization is a technique of generating short and accurate summary of a longer text document. Text summarization can be classified based on the number of input documents (single document and multi-document summarization) and based on the characteristics of the summary generated (extractive and abstractive summarization). Multi-document summarization is an automatic process of creating relevant, informative and concise summary from a cluster of related documents. This paper does a detailed survey on the existing literature on the various approaches for text summarization. Few of the most popular approaches such as graph based, cluster based and deep learning-based summarization techniques are discussed here along with the evaluation metrics, which can provide an insight to the future researchers.

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.

EM Clustering Algorithm for Automatic Text Summarization

2011 ◽  
pp. 305-315 ◽  
Author(s):  
Yulia Ledeneva ◽  
René García Hernández ◽  
Romyna Montiel Soto ◽  
Rafael Cruz Reyes ◽  
Alexander Gelbukh
Keyword(s):  
Clustering Algorithm ◽  
Text Summarization ◽  
Automatic Text Summarization ◽  
Em Clustering ◽  
Automatic Text
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