Deep Learning Based Hybrid Technique for Improved Summarization of Abstractive Text

Abstract

Аbstrасt: From various source sentences, construct summary sentences by merging facts. The process of preserving information material and overall meaning while condensing it into a shorter representation is known as abstractive text summarization, or ATS. It takes a lot of effort and time for humans to manually summarise large textual publications. In this paper, we present an ATS framework (ATSDL) based on (Long Short-Term Memory-Convolutional Neural Network) LSTM- CNN that can generate new sentences by investigating semantic phrases, which are finer- grained fragments than sentences. ATSDL, in contrast to current abstraction- based methods, consists of two primary phases: the first extracts phrases from source sentences, and the second uses deep learning to produce text summaries. Experimental results on CNN and Daily Mail datasets show that our ATSDL framework outperforms the state-of-the-art models in terms of both syntactic structure and semantics, and achieves competitive results on manual linguistic quality evaluation. In this application hybrid model is giving better performance over other state of art techniques.