Using Lexical Chains for Efficient Text Summarization

2009 ◽  
Author(s):  
Anthony-Claret Onwutalobi
Keyword(s):  
Text Summarization ◽  
Lexical Chains

scholarly journals Efficiently Computed Lexical Chains as an Intermediate Representation for Automatic Text Summarization

2002 ◽  
Vol 28 (4) ◽  
pp. 487-496 ◽  
Author(s):  
H. Gregory Silber ◽  
Kathleen F. McCoy
Keyword(s):  
Linear Time ◽  
Time Algorithm ◽  
Text Summarization ◽  
Intermediate Step ◽  
Intermediate Representation ◽  
Efficient Tool ◽  
Automatic Text Summarization ◽  
Lexical Chains ◽  
Lexical Chain ◽  
Automatic Text

While automatic text summarization is an area that has received a great deal of attention in recent research, the problem of efficiency in this task has not been frequently addressed. When the size and quantity of documents available on the Internet and from other sources are considered, the need for a highly efficient tool that produces usable summaries is clear. We present a linear-time algorithm for lexical chain computation. The algorithm makes lexical chains a computationally feasible candidate as an intermediate representation for automatic text summarization. A method for evaluating lexical chains as an intermediate step in summarization is also presented and carried out. Such an evaluation was heretofore not possible because of the computational complexity of previous lexical chains algorithms.

Extractive Technique for Text Summarization based on Ranking Scheme

2018 ◽  
Vol 6 (4) ◽  
pp. 369-373
Author(s):  
A.A. Shrivastava ◽  
◽  
◽  
◽  
A.S. Bagora ◽  
...  
Keyword(s):  
Text Summarization ◽  
Ranking Scheme

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.

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