Audio Summarization of Podcasts

Year : 2024 | Volume : 11 | Issue : 03 | Page : 18 26
    By

    Devika Rani Roy,

  • Sakshi Narendra Patil,

  • Ritika Baban Parate,

  • Tanaya More,

  1. Professor, Department of Information Technology, K.C. College of Engineering and Management studies and Research, Thane, Maharashtra, India
  2. Student, Department of Information Technology, K.C. College of Engineering and Management studies and Research, Thane, Maharashtra, India
  3. Student, Department of Information Technology, K.C. College of Engineering and Management studies and Research, Thane, Maharashtra, India
  4. Student, Department of Information Technology, K.C. College of Engineering and Management studies and Research, Thane, Maharashtra, India

Abstract

Podcasts have emerged as a significant medium for disseminating information, sharing stories, and providing entertainment. As their popularity continues to soar, the sheer volume of available content poses a challenge for listeners seeking to efficiently consume information. In this context, creating and deploying an audio summarizer for podcasts becomes highly significant. This research paper delves into the motivation for creating such a tool, emphasizing the increasing need for concise and informative summaries that can help users navigate the vast array of podcast episodes more effectively. By providing an overview of the methodology used for summarization, the paper outlines the steps involved in distilling key points from lengthy audio content. It also delves into the technological foundations enabling audio summarization, such as advancements in natural language processing, machine learning algorithms, and speech-to-text technology. Additionally, the paper discusses potential applications of an audio summarizer, highlighting its utility for various user groups such as busy professionals, students, and casual listeners who aim to stay informed without investing extensive time. Overall, the development of an audio summarizer for podcasts represents a crucial innovation in enhancing the accessibility and efficiency of information consumption in the digital age.

Keywords: Audio summarizer, podcast summarizer, natural language processing (NLP), automatic speech recognition (ASR), text-to-speech (TTS), content discovery, podcast transcription, text summarization, podcasting technology, audio processing

[This article belongs to Journal of Software Engineering Tools & Technology Trends ]

How to cite this article:
Devika Rani Roy, Sakshi Narendra Patil, Ritika Baban Parate, Tanaya More. Audio Summarization of Podcasts. Journal of Software Engineering Tools & Technology Trends. 2024; 11(03):18-26.
How to cite this URL:
Devika Rani Roy, Sakshi Narendra Patil, Ritika Baban Parate, Tanaya More. Audio Summarization of Podcasts. Journal of Software Engineering Tools & Technology Trends. 2024; 11(03):18-26. Available from: https://journals.stmjournals.com/josettt/article=2024/view=172139


References

  1. Rista A, Kadriu A. Automatic speech recognition: A comprehensive survey. SEEU Rev. 2020;15:86–112. DOI: 10.2478/seeur-2020-0019.
  2. Liya BS, Seenuvasan V, Sathya Prakash K, Siva B. Audio summarization in real time for podcast, speeches and audio books. J Surv Fish Sci. 2023;10:2618–25.
  3. Rachabathuni PK. A survey on abstractive summarization techniques. 2017 International Conference on Inventive Computing and Informatics (ICICI), Coimbatore, India, 2017. p. 762–5. DOI: 10.1109/ICICI.2017.8365239.
  4. Biswas PK, Iakubovich A. Extractive summarization of call transcripts. IEEE Access. 2022;10:119826–40. DOI: 10.1109/ACCESS.2022.3221404.
  5. Vanjari N, Pinjari S, Labde S, Patil P, Patil P, Sahitya A. Podcast summarization system. 2022 5th International Conference on Advances in Science and Technology (ICAST), Mumbai, India, 2022. p. 379–82. DOI: 10.1109/ICAST55766.2022.10039668.
  6. Khanna D, Bhushan R, Goel K, Juneja S. SummarizeAI – Summarization of the Podcasts. Available at SSRN 4628657. SSRN Electron J. 2023. DOI: 10.2139/ssrn.4628657.
  7. Lee SW, Ralph TC, Jeong H. Fundamental building block for all-optical scalable quantum networks. Phys Rev A. 2019;100:052303. DOI: 10.1103/PhysRevA.100.052303.
  8. Vartakavi A, PodSumm GA. Podcast Audio Summarization. arXiv Preprint ArXiv:2009.10315. 2020 Sep 22.
  9. Zhang Y, Ma Q. Dual attention model for citation recommendation with analyses on explainability of attention mechanisms and qualitative experiments. Comput Linguist. 2022;48:403–70. DOI: 10.1162/coli_a_00438.
  10. Mehta V, Deshpande T, Pandey R, Kandoi T, Nair S. Video transcript extraction and summarization using transfer learning. In: International Conference on Information and Communication Technology for Competitive Strategies. Springer Nature Singapore; 2022. p. 625–36.
  11. Laban P, Ye E, Korlakunta S, Canny J, Hearst M. Newspod: Automatic and interactive news podcasts. In: Proceedings of the 27th International Conference on Intelligent User Interfaces. 2022 Mar 22. p. 691–706. DOI: 10.1145/3490099.3511147.
  12. Al-Fraihat D, Sharrab Y, Alzyoud F, Qahmash A, Tarawneh M, Maaita A. Speech recognition utilizing deep learning: A systematic review of the latest developments. Hum-Centric Comput Inf Sci. 2024;14.
  13. Esmaeilzadeh S, Peh GX, Xu A. Neural Abstractive Text Summarization and Fake News Detection. arXiv Preprint ArXiv:1904.00788. 2019 Mar 24.
  14. Nallapati R, Zhou B, Gulcehre C, Xiang B. Abstractive Text Summarization Using Sequence-to-Sequence Rnns and Beyond. arXiv Preprint ArXiv:1602.06023. 2016 Feb 19.
  15. El-Kassas WS, Salama CR, Rafea AA, Mohamed HK. Automatic text summarization: A comprehensive survey. Expert Syst Appl. 2021;165:113679. DOI: 10.1016/j.eswa.2020.113679.
Regular Issue Subscription Review Article
Volume 11
Issue 03
Received 28/06/2024
Accepted 02/08/2024
Published 14/09/2024
320

Login


My IP

PlumX Metrics