A Review on Swarm Intelligence Based Approach for Automatic Document Summarization

2021 ◽  
Author(s):  
I Made Widiartha ◽  
Rukmi Sari Hartati ◽  
Nyoman Putra Sastra ◽  
Dewa Made Wiharta
Keyword(s):  
Swarm Intelligence ◽  
Document Summarization ◽  
Automatic Document Summarization

scholarly journals Semantic Similarity Based Automatic Document Summarization Method

2019 ◽  
Vol 8 (6) ◽  
pp. 2516-2522
Keyword(s):  
Semantic Similarity ◽  
Research Method ◽  
Clustering Algorithm ◽  
Performance Metrics ◽  
Research Work ◽  
Document Summarization ◽  
Summarization Method ◽  
Similarity Computation ◽  
Automatic Document Summarization ◽  
Fuzzy Genetic

Document summarization is the process of generating the summary of the documents gathered from the web sources. It reduces the burden of web readers by reducing the necessity of reading the entire document contents by generating the short summary. In our previous research work this is performed by introducing the method namely Noun weight based Automated Multi-Document Summarization method (NW-AMDSM). However the previous research work doesn’t concentrate on the semantic similarity which might reduce the accuracy of the summarization outcome. This is resolved in the proposed research method by introducing the method namely Semantic Similarity based Automatic Document Summarization Method (SS-ADSM). In this research work, multi document grouping is done is based on semantic similarity computation, thus the document with similar contents can be grouped more accurately. Here the semantic similarity computation is performed with the help of word net analyzer. The document grouping is done by introducing the modified FCM clustering algorithm. Finally hybrid neuro fuzzy genetic algorithm is introduced to perform the automatic summarization. The numerical analysis of the proposed research method is conducted in the matlab simulation environment and compared with other research methods in terms various performance metrics. The simulation analysis proved proposed method tends to have better performance in terms of increased accuracy of document summarization outcome.

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%.

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