Automatic Summarization from Multiple Documents

Multidocument Summarization of Engineering Papers Based on Macro- and Microstructure

Journal of Computing and Information Science in Engineering ◽

10.1115/1.3563048 ◽

2011 ◽

Vol 11 (1) ◽

Author(s):

Jiaming Zhan ◽

Ying Liu ◽

Han Tong Loh

Keyword(s):

Experimental Study ◽

Classification Problem ◽

Equivalence Classes ◽

Automatic Summarization ◽

Categorization Task ◽

Rhetorical Structure ◽

Evaluation Scores ◽

Multidocument Summarization ◽

Multiple Documents

This paper focuses on automatic summarization of multiple engineering papers. A summarization approach based on documents’ macro- and microstructure has been proposed. The macrostructure consists of a list of ranked topics from engineering papers. Topics are discovered by extracting and grouping frequently appearing word sequences into equivalence classes. Hence, the macrostructure symbolically presents the topical links in different papers. Meanwhile, the microstructure is defined as the rhetorical structure within a single paper. The identification of microstructure is approached as a classification problem. Each sentence in a paper is automatically labeled with one of the predefined rhetorical categories. Unlike existing summarization methods that first separate documents into nonoverlapping clusters and then summarize each cluster individually, our approach aims to summarize multiple documents according to the characteristics suggested at macro- and microstructure levels. The experimental study showed that our proposed approach outperformed peer systems in terms of recall-oriented understudy for gisting evaluation scores and readers’ responsiveness. In an independent manual categorization task using the summaries generated by our approach and peer systems, we also performed better in terms of precision and recall.

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

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190716105347 ◽

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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