Paper Title
Text Summarization Using Multi-Relational Graphs and Text Generation Techniques

Topic of text summarization has been a hot topic in the field of natural language processing and will continue to play a hot research based topic role in our information overloaded world where people don’t have time to go through the whole text available to them whether the information is from the internet or the daily newspaper,most of people want a brief summary of the text without having to read through the whole document and our solution to the chosen problem statement may provide a solution to the information overload problem. Text summarization in natural language processing focuses on the process of using algorithms to briefly describe the large bodies of text without compromising on the gist of the document with the summary being accurate,unbiased and emphasizing on the key facts of the text. Our technique proposes a Graph convolution network algorithm and clustering based summarization approach. The proposed approach consists of three main steps: Pre-processing of the dataset.Then Building Graph Convolution network (GCN) model to learn the syntactic representation for a document. The selective attention mechanism is used to extract salient information in semantic and structural aspects and generate an extractive summary. Same model is applied on the dataset to obtain an extractive summary. The novelty of our proposed solution to the problem is to show that the summarization result not only depends on the sentence features, but also depends on the sentence similarity measure. GCN takes into account not only the semantic and structural aspects of the text document and provides the summary. The evaluation metric and the result obtained by training the GCN model on the BBC dataset shows that our proposed approach can improve the performance compared to other traditional methods of summarization. Keywords - GCN, BBC