Abstract

In today\'s digital era, the influx of video content inundates online platforms daily, presenting a formidable challenge for users seeking relevant information within these extensive recordings, particularly under time constraints. The process of extracting crucial insights from lengthy videos often proves daunting and time- consuming. Clip Outliner is an innovative project that offers a versatile solution for summarizing both textual and video content. This tool combines the capabilities of a text summarizer with advanced video analysis features, providing users with concise textual summaries while also offering insights into video content. Leveraging Python APIs for text transcription and employing natural language processing (NLP) techniques, transcripts are efficiently condensed. The user interface is meticulously crafted using Streamlit serving as the robust open-source framework in Python.

Introduction

I. INTRODUCTION

Clip Outliner is an advanced tool that combines video and text summarization capabilities, providing users with concise textual summaries of multimedia content.

With the sheer volume of video recordings flooding online platforms daily, extracting meaningful insights from this vast sea of information has become increasingly daunting, especially when time is of the essence. Traditional methods of manually sifting through lengthy videos to distill key points are often inefficient and time-consuming,

The main goal/objective of the Clip Outliner project,

is to streamline the process of content comprehension and analysis for users dealing with multimedia content particularly videos and texts [1].

Leveraging Python, natural language processing (NLP), NLTK, Streamlit, and API technologies [2], it offers features such as video analysis for sensitive content detection and sponsorship identification, along with word analysis functionalities like synonyms, antonyms, and word meanings [3]. This versatile platform streamlines the process of extracting key insights from videos and texts, enhancing content comprehension and analysis for users across various domains [4].

Additionally, by incorporating word analysis functionalities such as synonyms, antonyms, and word meanings, Clip Outliner enhances user understanding and comprehension of textual content. Overall, the project's primary goal is to facilitate efficient and effective content summarization and analysis, ultimately saving users time and effort in navigating and interpreting multimedia content [5].

II. LITERATURE SURVEY

The development of Clip Outliner aligns with existing literature on multimedia content summarization, natural language processing (NLP), and text analysis, as well as the challenges and opportunities presented by the digital age. While existing text summarization models utilize various methods such as abstractive and extractive techniques, they ultimately aim to produce similar outputs to our model: condensed and informative text summaries. However, what sets our model apart is its unique feature of not only converting non-transcripted video content into text but also striving to make these summarized texts available in multiple languages. Some examples of relevant literature are given below:

1. “Summary and Keyword Extraction from YouTube video Transcript” this research paper was published by Shraddha Yadav, Arun Kumar Behra, Chandra Shekhar Sahu, Nilmani Chandraka focuses on leveraging Natural Language Processing techniques for summarizing video transcripts and extracting key keywords [6]. By employing both extractive and abstractive summarization methods, the study aims to condense essential information from video content [7].
2. Aniqa Dilawari and Muhammad Usman Ghani Khan created "Abstractive Summarization of video Sequences" employed the RCNN deep neural network model. The approach primarily focuses on the succinctness of the summary, overlooking considerations for time restrictions and memory efficiency [8].
3. Parth Rajesh Dedhia, Hardik Pradeep, and Meghana Naik created "Research on Abstractive Text Summarization Methods". It was published in 2020. In this model seq2seq, Encoder-Decoder, and Pointer Mechanism is utilized [9].
4. In 2021, “Natural Language Processing (NLP) based Text Summarization - A Survey” was published by Ishitva Awasthi, K Gupta, P S Bhojal, Piyush Kumar. Their study emphasizes the computation of sentence implications through linguistic and statistical feature analysis [10].

III. PROPOSED METHODOLOGY

The proposed methodology for the "Clip Outliner" outlines a comprehensive approach to developing a versatile video and text summarization tool.

1. The data collection from YouTube videos and preprocessing of extracted transcripts.
2. Abstractive summarization is done using Hugging Face transformer. Word analysis functionality is done using nltk
3. Integration of the Assembly API facilitates YouTube video analysis, enabling the detection of sensitive content and sponsorships through computer vision algorithms.

IV. ??????? MODULE DESCRIPTION

Through this module, users can seamlessly obtain concise summaries of video content without the need to manually sift through lengthy transcripts.

1. Provides an interactive user interface, to make it easy for user.
2. Allows user to input the text/video that they want to summarize, and get its summary, along with text to speech feature.
3. Enhanced by video analysis feature, where videos are analyzed, summary is generated, important topics are covered.
4. Along with that translation of text to any language, word meaning, antonym/synonym can to found.

V. IMPLEMENTATION

Clip Outliner is an advanced tool that combines video and text summarization capabilities, providing users with concise textual summaries of multimedia content. Leveraging Python, natural language processing (NLP), NLTK, Streamlit, and API technologies, it offers features such as video analysis for sensitive content detection and sponsorship identification, along with word analysis functionalities like synonyms, antonyms, and word meanings. This versatile platform streamlines the process of extracting key insights from videos and texts, enhancing content comprehension and analysis for users across various domains.

The implementation of the Clip Outliner system encompasses the integration of various technologies and methodologies to create a robust and user-friendly platform for video and text summarization. By providing efficient summarization tools for both video and text content, along with features like video analysis for sensitive content detection and sponsorship identification, the project aims to empower users to quickly extract valuable insights from large volumes of multimedia data.

Additionally, by incorporating word analysis functionalities such as synonyms, antonyms, and word meanings, Clip Outliner enhances user understanding and comprehension of textual content. Overall, the project's primary goal is to facilitate efficient and effective content summarization and analysis, ultimately saving users time and effort in navigating and interpreting multimedia content.

???????VI. RESULT

Clip Outliner project represents a significant advancement in the field of content summarization and analysis. By integrating cutting-edge technologies such as the Hugging Face Transformer library, NLTK, and Assembly API, the system offers users a comprehensive solution for extracting key insights from both video and text content.

The implementation of abstractive summarization techniques, word analysis functionalities, and video analysis features enhances the efficiency and effectiveness of the platform. Below is the output when text summarizer is selected, as video summarizer implementation is shown in figure 4.

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Conclusion

Clip Outliner offers users a range of benefits, notably saving time by condensing lengthy video and text content into concise summaries. Its advanced natural language processing and word analysis features enrich comprehension, while the video analysis function aids content moderation. With a user-friendly interface, Clip Outliner caters to users of all levels and enhances collaboration by facilitating efficient information retrieval and sharing. Its versatility makes it applicable across various domains, from academic research to content creation and marketing. Overall, Clip Outliner streamlines content comprehension and analysis, empowering users to extract valuable insights efficiently. In the realm of content summarization and analysis, the future scope of Clip Outliner promises a host of exciting developments. Potential avenues for growth include expanding its capabilities to handle multi- document summarization, thereby enabling users to efficiently extract insights from larger datasets. Enhanced video analysis features, integrating advanced computer vision algorithms, could provide deeper insights into video content, including sentiment analysis and object recognition. Personalization options and real-time summarization capabilities would further enhance user experience and decision-making efficiency. Integrating with external platforms and continuously refining underlying models will ensure Clip Outliner remains at the forefront of content summarization technology, offering users unparalleled capabilities in navigating and extracting insights from multimedia content. Additionally, implementing support for multiple languages would broaden its user base and facilitate cross-linguistic content analysis.

References

[1] Barzilay, R., Elhadad, M.: Using lexical chains for text summarization. In: Proceedings of the ACL/EACL’97 Workshop on Intelligent Scalable Text Summarization, Madrid, pp. 10–17 (1997). [2] Bossard, A., Généreux, M., Poibeau, T.: Cbseas, a summarization system – integration of opinion mining techniques to summarize blogs. In: Proceedings of the 12th Meeting of the European Association for Computational Linguistics (system demonstration), EACL ’09, Athens. Association for Computational Linguistics, Stroudsburg (2009). [3] DeJong, G.: An overview of the FRUMP system. In: Lehnert, W., Ringle, M. (eds.) Strategies for Natural Language Processing, pp. 149–176. Lawrence Erlbaum Associates, Hillsdale. [4] Amigó E, Gonzalo J, Penas A, Verdejo F (2005) QARLA: a framework for the evaluation of text summarization systems. In: ACL ’05: proceedings of the 43rd annual meeting on association for computational linguistics, pp 280– 289 [5] Banerjee S Mitra P, Sugiyama K (2015) Multi-document abstractive summarization using ILP based multi-sentence compression. In: Proceedings of the 24th international joint conference on artificial intelligence (IJCAI 2015), pp 1208–1214. [6] Building RESTFUL APIs https://atmamani.github.io/blog/building- restful-apis-with-flask-inpython/ Accessed on:28/01/2022

Copyright

Copyright © 2024 Nazia Sheikh, Shreoshi Roy, Dr. Sadhana Rana . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.