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

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%.

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.

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 Prediction and Analysis of Extracting Relations using Spacy Model

2020 ◽  
Vol 8 (6) ◽  
pp. 3281-3287
Keyword(s):  
Natural Language ◽  
Information Extraction ◽  
Performance Measures ◽  
Text Summarization ◽  
Language Understanding ◽  
Language Generation ◽  
Automatic Text Summarization ◽  
Structured Information ◽  
Automatic Text ◽  
F Measure

Text is an extremely rich resources of information. Each and every second, minutes, peoples are sending or receiving hundreds of millions of data. There are various tasks involved in NLP are machine learning, information extraction, information retrieval, automatic text summarization, question-answered system, parsing, sentiment analysis, natural language understanding and natural language generation. The information extraction is an important task which is used to find the structured information from unstructured or semi-structured text. The paper presents a methodology for extracting the relations of biomedical entities using spacy. The framework consists of following phases such as data creation, load and converting the data into spacy object, preprocessing, define the pattern and extract the relations. The dataset is downloaded from NCBI database which contains only the sentences. The created model evaluated with performance measures like precision, recall and f-measure. The model achieved 87% of accuracy in retrieving of entities relation.

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