Summarization of Text and Image Captioning in Information Retrieval Using Deep Learning Techniques

Abstract

Automated information retrieval and text summarization concept is a difficult process in natural language processing because of the infrequent structure and high complexity of the documents. The text summarization process creates a summary by paraphrasing a long text. Earlier models on information retrieval and summarization are based on a massive labeled dataset by the use of handcrafted features, leveraging on knowledge for a particular domain, and concentrated on the narrow sub-domain to improve efficiency. This paper presents a new deep learning (DL) based information retrieval with a text summarization model. The proposed model involves three major processes namely information retrieval, template generation, and text summarization. Initially, the bidirectional long short term memory (BiLSTM) approach is employed for retrieving the textual data, which assumes each word in a sentence, extracts the information, and embeds it into the semantic vector. Next, the template generation process takes place using the DL model. The deep belief network (DBN) model is employed as a text summarization tool to summarize the textual content. In addition, the image description is generated for the visualized entities that exist in the images. The design of BiLSTM with the DBN model for the text summarization and image captioning process shows the novelty of the work. The performance of the presented method is validated using Giga word corpus and DUC corpus. The experimental results referred that the proposed DBN model outperformed the compared methods with the maximum precision, recall and F-score. The image captions are compared with a predefined set of captions that exists for the image and the performance is evaluated using the BLEU metric.