Semantic-Based Summarization Using Ontology and Machine Learning Algorithms

Abstract

In natural language processing, the task of text summarization is a challenging and intriguing problem. A summary of a long document enables the reader to easily grasp the key informa- tion without having to read the entire text. The existing methods struggle with generating fluency and coherent summaries, often missing important sentences in a document. This the- sis aims to automate text summarization by using two approaches: extractive and abstractive. In the extractive text summarization, we introduce two layers of sentence scoring. First, we identify different features of the sentences based on a story document and assign weights to them. Second, we readjust the scoring by applying various rules. The top-N sentences are selected as our extractive text summary, where all the sentences have a high score. In an abstractive text summary, we utilize a predefined T5 model, comparing it with Bert2Bert, PEAGAUS, BART, and GPT2 models. The summary of the document is created based on the level of importance of the sentences in the document. The results obtained using the T5 on the feature-extraction with rules refinement scoring dataset are competitive with state-of-the-art. For the FER model, the performance has been improved, among which the most significant improvements are: BERTScoring 0.88, 0.80, 0.83, for the three selected story documents. After these utilization we also create a FER ontology their each sentences is work as instance and their score are work as its data properties. By using this technique we make our model to query-based user intension summary generator. This ontology give the same result as we pass a query that calculate all the feature and give the high score sentences as an output. By using this technique user also get the summary as their need.