The Great Importance of Cross-Document Relationships for Multi-document Summarization

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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Multi-document Summarization using Semantic Role Labeling and Semantic Graph for Indonesian News Article

2020 7th International Conference on Advance Informatics: Concepts, Theory and Applications (ICAICTA) ◽

10.1109/icaicta49861.2020.9429055 ◽

2020 ◽

Author(s):

Yuly Haruka Berliana Gunawan ◽

Masayu Leylia Khodra

Keyword(s):

News Article ◽

Semantic Role ◽

Semantic Role Labeling ◽

Document Summarization ◽

Semantic Graph

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Street Food Stand Availability, Density, and Distribution Across Income Levels in Mexico City

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18083953 ◽

2021 ◽

Vol 18 (8) ◽

pp. 3953

Author(s):

Jose B. Rosales Chavez ◽

Meg Bruening ◽

Punam Ohri-Vachaspati ◽

Rebecca E. Lee ◽

Megan Jehn

Keyword(s):

Mexico City ◽

Food Environment ◽

Local School ◽

Middle Income ◽

Street Food ◽

Income Levels ◽

Rigorous Approach ◽

Ground Truthing ◽

Document Relationships ◽

Street Segments

Street food stands (SFS) are an understudied element of the food environment. Previous SFS studies have not used a rigorous approach to document the availability, density, and distribution of SFS across neighborhood income levels and points of access in Mexico City. A random sample (n = 761) of street segments representing 20 low-, middle-, and high-income neighborhoods were assessed using geographic information system (GIS) and ground-truthing methods. All three income levels contained SFS. However, SFS availability and density were higher in middle-income neighborhoods. The distribution of SFS showed that SFS were most often found near homes, transportation centers, and worksites. SFS availability near schools may have been limited by local school policies. Additional studies are needed to further document relationships between SFS availability, density, and distribution, and current structures and processes.

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