New graph-based text summarization method

2015 ◽  
Author(s):  
Saif alZahir ◽  
Qandeel Fatima ◽  
Martin Cenek
Keyword(s):  
Text Summarization ◽  
Summarization Method

An Automatic Text Summarization Method with the Concern of Covering Complete Formation

2020 ◽  
Vol 13 (5) ◽  
pp. 977-986
Author(s):  
Srinivasa Rao Kongara ◽  
Dasika Sree Rama Chandra Murthy ◽  
Gangadhara Rao Kancherla
Keyword(s):  
Research Method ◽  
Research Work ◽  
Fuzzy Rule ◽  
Text Summarization ◽  
Document Summarization ◽  
Summarization Method ◽  
Overall Evaluation ◽  
Multiple Documents ◽  
Rule System ◽  
Value Decomposition

Background: Text summarization is the process of generating a short description of the entire document which is more difficult to read. This method provides a convenient way of extracting the most useful information and a short summary of the documents. In the existing research work, this is focused by introducing the Fuzzy Rule-based Automated Summarization Method (FRASM). Existing work tends to have various limitations which might limit its applicability to the various real-world applications. The existing method is only suitable for the single document summarization where various applications such as research industries tend to summarize information from multiple documents. Methods: This paper proposed Multi-document Automated Summarization Method (MDASM) to introduce the summarization framework which would result in the accurate summarized outcome from the multiple documents. In this work, multi-document summarization is performed whereas in the existing system only single document summarization was performed. Initially document clustering is performed using modified k means cluster algorithm to group the similar kind of documents that provides the same meaning. This is identified by measuring the frequent term measurement. After clustering, pre-processing is performed by introducing the Hybrid TF-IDF and Singular value decomposition technique which would eliminate the irrelevant content and would result in the required content. Then sentence measurement is one by introducing the additional metrics namely Title measurement in addition to the existing work metrics to accurately retrieve the sentences with more similarity. Finally, a fuzzy rule system is applied to perform text summarization. Results: The overall evaluation of the research work is conducted in the MatLab simulation environment from which it is proved that the proposed research method ensures the optimal outcome than the existing research method in terms of accurate summarization. MDASM produces 89.28% increased accuracy, 89.28% increased precision, 89.36% increased recall value and 70% increased the f-measure value which performs better than FRASM. Conclusion: The summarization processes carried out in this work provides the accurate summarized outcome.

scholarly journals Automatic Text Summarization Based on Semantic Networks and Corpus Statistics

2019 ◽  
Vol 13 (2) ◽  
pp. 137
Author(s):  
Winda Yulita ◽  
Sigit Priyanta ◽  
Azhari SN
Keyword(s):  
Semantic Networks ◽  
Online News ◽  
Research Data ◽  
Text Summarization ◽  
Automatic Text Summarization ◽  
Summarization Method ◽  
Best Value ◽  
The Difference ◽  
Automatic Text

One simple automatic text summarization method that can minimize redundancy, in summary, is the Maximum Marginal Relevance (MMR) method. The MMR method has the disadvantage of having parts that are separated from each other in summary results that are not semantically connected. Therefore, this study aims to compare summary results using the MMR method based on semantic and non-semantic based MMR. Semantic-based MMR methods utilize WordNet Bahasa and corpus in processing text summaries. The MMR method is non-semantic based on the TF-IDF method. This study also carried out summary compression of 30%, 20%, and 10%. The research data used is 50 online news texts. Testing of the summary text results is done using the ROUGE toolkit. The results of the study state that the best value of the f-score in the semantic-based MMR method is 0.561, while the best f-score in the non-semantic MMR method is 0.598. This value is generated by adding a preprocessing process in the form of stemming and compression of a 30% summary result. The difference in value obtained is due to incomplete WordNet Bahasa and there are several words in the news title that are not in accordance with EYD (KBBI).

scholarly journals A Semantic Text Summarization Method using ontology based Knowledge

2020 ◽  
Vol 6 (11) ◽  
pp. 122-125
Author(s):  
Dr.A.Mekala
Keyword(s):  
Data Mining ◽  
Health Care ◽  
Information Source ◽  
Information Access ◽  
Text Summarization ◽  
Common Information ◽  
Related Information ◽  
Princeton University ◽  
Summarization Method ◽  
User Query

Data mining is a method which finds useful patterns from large amount of data. As vast amounts of information are created quickly, effective information access becomes an important matter. Particularly for important domains, such as health check and monetary areas, well-organized recovery of succinct and related information is highly desired. In this paper we propose a new user query based text summarization technique that makes use of WordNet, a common information source from Princeton University. Our summarization structure is expressly tuned to recapitulate health care documents.

Text Summarization Using Hybrid Parallel Genetic Algorithm

2011 ◽  
Vol 271-273 ◽  
pp. 154-157
Author(s):  
Xin Lai Tang ◽  
Xiao Rong Wang
Keyword(s):  
Genetic Algorithm ◽  
Vector Space ◽  
Traditional Method ◽  
Vector Space Model ◽  
Text Summarization ◽  
Parallel Genetic Algorithm ◽  
Automatic Summarization ◽  
Space Model ◽  
Evaluation Scheme ◽  
Summarization Method

This paper proposes a special Chinese automatic summarization method based on Concept-Obtained and Improved K-means Algorithm. The idea of our approach is to obtain concepts of words based on HowNet, and use concept as feature, instead of word. We use conceptual vector space model and Improved K-means Algorithm to form a summarization. Experimental results indicate a clear superiority of the proposed method over the traditional method under the proposed evaluation scheme.

scholarly journals Multi-Documents Extractive Text Summarization using Node Centrality

2020 ◽  
Vol 9 (3) ◽  
pp. 2817-2822
Keyword(s):  
Social Media ◽  
Text Summarization ◽  
The Internet ◽  
Hidden Information ◽  
Extractive Summarization ◽  
Amount Of Information ◽  
Vast Amount ◽  
Node Centrality ◽  
Summarization Method ◽  
Katz Centrality

The advancement of technologies produce vast amount of data over the internet. The massive amount of information flooded in the webpages become more difficult to extract the meaningful insights. Social media websites are playing major role in publishing news events on the similar topic with different contents. Extracting the hidden information from the multiple webpages are tedious job for researchers and industrialists. This paper mainly focuses on gathering information from multiple webpages and to produce summary from those contents under similar topic. Multi-document extractive summarization has been developed using the graph based text summarization method. Proposed method builds a graph between the multi-documents using the Katz centrality of nodes. The performance of proposed GeSUM (Graph based Extractive Summarization) is evaluated with the ROUGE metrics.

scholarly journals Recent Trends in Deep Learning Based Abstractive Text Summarization

2019 ◽  
Vol 8 (3) ◽  
pp. 3108-3115 ◽  
Keyword(s):  
Language Processing ◽  
Large Data ◽  
Text Summarization ◽  
Accurate Analysis ◽  
Summarization Method ◽  
Text Information ◽  
Recent Trends ◽  
Correct Understanding ◽  
Abstractive Summarization ◽  
Knowledge Exploration

With the rapid growth of cyberspace and the appearance of knowledge exploration era, good text summarization method is vital to reduce the large data. Text summarization is the mechanism of extracting the important information which gives us an overall abstract or summary of the entire document and also reduces the size of the document. It is open problem in Natural Language Processing (NLP) and a difficult work for humans to understand and generate an abstract manually while it have need of a accurate analysis of the document. Text Summarization has become an important and timely tool for assisting and interpreting text information. It is generally distinguished into: Extractive and Abstractive. The first method directly chooses and outputs the relevant sentences in the original document; on the other hand, the latter rewrites the original document into summary using NLP techniques. From these two methods, abstractive text summarization is laborious task to realize as it needs correct understanding and sentence amalgamation. This paper gives a brief survey of the distinct attempts undertaken in the field of abstractive summarization. It collectively summarizes the numerous technologies, difficulties and problem of abstractive summarization

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