Text Summarization Using Hybrid Parallel Genetic Algorithm

2011 ◽  
Vol 268-270 ◽  
pp. 1073-1076
Author(s):  
Xin Lai Tang ◽  
Xiao Rong Wang ◽  
Meng Wang
Keyword(s):  
Genetic Algorithm ◽  
Traditional Method ◽  
Text Summarization ◽  
Experimental Results ◽  
Parallel Genetic Algorithm ◽  
Evaluation Scheme ◽  
Summarization Method

This paper proposes a special text summarization method based on hybrid parallel genetic algorithm. The idea of our approach is to obtain center of sentences based on k-means clustering and hybrid parallel genetic algorithm. We select those sentence according its importance and distance of center to form a summarization. Experimental results indicate a clear superiority of the proposed method over the traditional method under the proposed evaluation scheme.

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.

Implementation of Multi-Core Parallel Computation for Real-Time Turning of PID Parameters Based on Genetic Algorithm

2012 ◽  
Vol 159 ◽  
pp. 132-136
Author(s):  
Shuang Shi Zhang ◽  
Xu Ben Wang ◽  
Wei Hua Wang
Keyword(s):  
Genetic Algorithm ◽  
Process Control ◽  
Real Time ◽  
Parallel Computation ◽  
Experimental Results ◽  
Parallel Genetic Algorithm ◽  
Control Computer ◽  
Process Control Computer ◽  
Better Than

The aim of the work described in this paper is to investigate the implementation of Multi-core Parallel Genetic Algorithm (McPGA) for real-time turning of PID parameters based on it using OpenMP. The performance of our scheme is discussed in comparison with that of the Sequential Genetic Algorithm (SGA) program running on the same computer. Numerical experimental results clearly show that McPGA is much better than SGA on convergence, premature and optimized speed. It can be used for PID parameter real-time turning in Industry Process Control Computer (IPC).

Research on text feature clustering based on an improved parallel genetic algorithm

2015 ◽  
Author(s):  
M. Y. Jiang ◽  
X. J. Fan ◽  
Y. X. Zhou ◽  
J. Lian ◽  
J. Q. Jiang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Parallel Genetic Algorithm ◽  
Feature Clustering ◽  
Text Feature

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.

Review Summary Generation in Online Systems: Frameworks for Supervised and Unsupervised Scenarios

2021 ◽  
Vol 15 (3) ◽  
pp. 1-33
Author(s):  
Wenjun Jiang ◽  
Jing Chen ◽  
Xiaofei Ding ◽  
Jie Wu ◽  
Jiawei He ◽  
...  
Keyword(s):  
Decision Making ◽  
Real World ◽  
Text Summarization ◽  
Experimental Results ◽  
Product Review ◽  
Comprehensive Review ◽  
Online Systems ◽  
Real World Datasets ◽  
Different Characteristics

In online systems, including e-commerce platforms, many users resort to the reviews or comments generated by previous consumers for decision making, while their time is limited to deal with many reviews. Therefore, a review summary, which contains all important features in user-generated reviews, is expected. In this article, we study “how to generate a comprehensive review summary from a large number of user-generated reviews.” This can be implemented by text summarization, which mainly has two types of extractive and abstractive approaches. Both of these approaches can deal with both supervised and unsupervised scenarios, but the former may generate redundant and incoherent summaries, while the latter can avoid redundancy but usually can only deal with short sequences. Moreover, both approaches may neglect the sentiment information. To address the above issues, we propose comprehensive Review Summary Generation frameworks to deal with the supervised and unsupervised scenarios. We design two different preprocess models of re-ranking and selecting to identify the important sentences while keeping users’ sentiment in the original reviews. These sentences can be further used to generate review summaries with text summarization methods. Experimental results in seven real-world datasets (Idebate, Rotten Tomatoes Amazon, Yelp, and three unlabelled product review datasets in Amazon) demonstrate that our work performs well in review summary generation. Moreover, the re-ranking and selecting models show different characteristics.

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