Multi-Modal Summarization of Read, Watch, Listen for Text and Multimedia Content
P Subhash1, Ram Mohan S A2
1Dr. P Subhash, Associate Professor, Department of Computer Science and Engineering VNRVJIET, Hyderabad India.
2Ram Mohan S A, M.Tech, Department of Computer Science and Engineering VNRVJIET, Hyderabad, India.
Manuscript received on 08 June 2019 | Revised Manuscript received on 13 June 2019 | Manuscript Published on 08 July 2019 | PP: 407-411 | Volume-8 Issue-8S3 June 2019 | Retrieval Number: H11000688S319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Conceptual Automatic content summarization is a main NLP apps that intends to consolidate a source content into a shorter adjustment. The quick addition in all kind of data show over the internet requires multi-modal summarization (MMS) from non-simultaneous aggregations of content, picture, sound and video. Here propose an extractive MMS procedure that joins the strategies of NLP, discourse handling and PC vision to examine the rich data contained all kind of data and to get better the idea of multimedia news summarization. The main idea is to associate the semantic openings between multimodel substance. Sound and visual are major modalities in the video. For sound data, we structure an approach to manage explicitly use its interpretation and to find the astounding nature of the translation with sound signals. For visual data, we get acquainted with the joint depictions of content and pictures using a neural framework. By then, we get the incorporation of the made framework for noteworthy visual data through content picture coordinating or multimodal topic showing. Finally, all the multimodal points are considered to make a literary once-over by increasing the striking nature, non-reiteration, clarity and consideration through the arranged streamlining of sub isolated limits.
Keywords: Summarization, Multimedia, Multi-modal, NLP
Scope of the Article: Multimedia Communications