A Novel Approach to Text Summarisation Using Topic Modelling and Noun Phrase Extraction

Over the past few years, one of the remarkable developments that happened on the web is the rapid growth of textual data. This substantial increase, however, induces a complication in the retrieval of vital information from the digitized collection of data. The conventional technique used to tackle this problem is Automatic Text Summarisation. This technique extracts the essential words or sentences from the data and summarises it without affecting the semantics. Automatic text summarisation is classified into two, Extractive and Abstractive. The Extractive method summarises a document by selecting the important words or sentences from it, based on some attributes while the Abstractive method attempts to generate its summary from the semantics of the data. In this paper, we propose a novel approach in Extractive text summarisation by using a new sentence scoring parameter. The experimental results show that the proposed sentence scoring parameter improves the performance of the Extractive text summariser, when compared with other summarisation models. To validate our proposed model, we compared it with four commonly used summarisation models on grounds of ROUGE-1 score and F1 score.