Enhanced sentence extraction through neuro-fuzzy technique for text document summarization

• Main Author: Ahmad Kamil, Muhammad Azhari
• Format: Thesis
• Language: English; English
• Published: 2021
• Subjects: Q Science (General); QA Mathematics
• Online Access: http://eprints.utem.edu.my/id/eprint/26064/; https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=121166

A summary system comprises a subtraction of text documents to generate a new form that delivers the essentials contents of the documents. Due to the hassle of documents overload, getting the right information and effectively-developed summaries are essential in retrieving information. Reduction of information allows users to find the information needed quickly without the need to read the full document collection, in particular, multi documents. In the recent past, soft computing-based approaches have gained popularity in its ability to determine important information across documents. A number of studies have modelled summarization systems based on fuzzy logic reasoning in order to select important sentences to be included in the summary. Although past studies support the benefits of employing fuzzy based reasoning for extracting important sentences from the document, there is a limitation concerning this method. Human or linguistic experts are required to determine the rules for the fuzzy system. Furthermore, the membership functions need to be manually tuned. These can be a very tedious and time-consuming process. Moreover, the performance of the fuzzy system can be affected by the choice of rules and parameters of membership function. Therefore, this study proposes a text summarization model based on classification using neuro-fuzzy approach. A classifier is first trained to identify summary sentences. Then, we use the proposed model to score and filter high-quality summary sentences. We compare the performance of our proposed model with the existing approaches, which are based on fuzzy logic and neural network techniques. In this study, we also evaluate the performance of sentence scoring and clustering in the process of generating text summaries. The proposed neuro-fuzzy model was used to score the sentences and clustering were performed using K-Means and Hierarchical Clustering (HC) approaches. The proposed approach showed improved results compared to the previous techniques in terms of precision, recall and F-measure on the Document Understanding Conference (DUC) data corpus. However, it was found that no improvements in the quality of the generated summaries obtained by simply performing clustering.