Integration Distance Similarity with Keyword Algorithm for Improving Cohesion between Sentences in Text Summarization

4th International Conference on Science, Technology and Interdisciplinary Research

工业技术（总论）;自然科学（总论）

Darmawan, Rizki^1 ; Wijaya, Adi^2

Computer Engineering Department, Sekolah Tinggi Teknik Muhammadiyah Cileungsi, Indonesia^1

Computer Engineering Department, Mohammad Husni Thamrin University, Indonesia^2

关键词: End users;    Exponential growth;    Extraction method;    Gold standards;    Text summarization;    Textual information;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/532/1/012019/pdf
DOI  :  10.1088/1757-899X/532/1/012019

来源: IOP

【 摘 要 】

In recent time the exponential growth of textual information available on the Web, end user need to be able to access information in summary form. Commonly the method to get the summary is extraction method. One of extraction method that easier and commonly used is Keyword Algorithm, but this algorithm has a weakness in the cohesion between the sentences. Distance similarity method is one method used for solving the cohesion problem. The idea of this paper is to improve cohesion between sentences based on extraction of keyword algorithm. The hybrid keyword algorithm and the distance similarity method is proposed. The proposed method was compared three distance similarity such as Cosine, Dice and Jaccard that looking for the cohesiveness between sentences according to keyword algorithm extraction and performance as standard of evaluation. The result showed that Dice has the highest cohesion degree is 45.87 %. Although the best performance is Cosine that performance is influenced with gold standard of abstractive human summary.