Copyright: © the author(s), publisher and licensee Technoscience Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non- commercial use, distribution, and reproduction in any medium, provided the original work is properly cited International Journal of Scientific Research in Computer Science, Engineering and Information Technology ISSN : 2456-3307 (www.ijsrcseit.com) doi : https://doi.org/10.32628/CSEIT2390218 202 Utilizing Deep Learning Techniques for Text and Image Capturing Summarization in Information Retrievals Dr. S. Selvakani* 1 , Mrs K. Vasumathi 2 , S.Divya 3 *1 Assistant Professor and Head, PG Department of Computer Science, Government Arts and Science College, Arakkonam, Tamil Nadu, India 2 Assistant Professor, PG Department of Computer Science, Government Arts and Science College, Arakkonam, Tamil Nadu, India 3 PG Scholar, PG Department of Computer Science, Government Arts and Science College, Arakkonam, Tamil Nadu, India A R T I C L E I N F O A B S T R A C T Article History: Accepted: 13 March 2023 Published: 29 March 2023 In this paper, a novel information retrieval and text summarization model based on deep learning (DL) is introduced. The model comprises three primary stages, including information retrieval, template generation, and text summarization. The initial step involves utilizing a bidirectional long short term memory (BiLSTM) technique to retrieve textual data. This approach considers each word in a sentence, extracts relevant information, and converts it into a semantic vector. Keywords: Semantics, Information retrieval, Feature extraction, Data mining, Deep learning, Task analysis. Publication Issue Volume 10, Issue 2 March-April-2023 Page Number 202-207 I. INTRODUCTION Due to the rapid growth of content such as blogs, articles, and reports, retrieving data from vast amounts of text has become an arduous task. Automatic text summarization methods offer a solution by extracting meaningful information from extensive texts, preserving the original meaning and significant portions. Summarization is a crucial aspect of natural language understanding, aiming to produce a concise representation of the input text that captures its essence. Extractive approaches, which involve selecting and combining text fragments, are commonly used in successful summarization systems. Conversely, abstractive summarization strives to generate a summary from scratch, incorporating new elements not present in the original text. Text-Image summarization involves summarizing a document containing both text and images into a