Automatic audio summarization using Natural Language Processing
DOI:
https://doi.org/10.61841/sgk6xa13Keywords:
Abstractive Text summarization, Audio Summarization, Natural Language Processing, Automatic Text summarization, Extractive Text summarizationAbstract
Audio summarization through text summarization is a very important application of Natural Language Processing(NLP). Whenever there is a conversation happening it involves various types of discussions. Important information always gets lost between such jumbled conversations. Consequently, it becomes extremely essential to extract those important key points for future reference. In this paper we aim to implement the automation of the process. The relevant information resulting from the conversation is extracted with the aid of Natural Language Processing techniques by employing text summarization approaches such as Extractive text summarization and Abstractive text summarization.
Downloads
References
1. Gupta V, Lehal GS. A survey of text summarization extractive techniques. Journal of emerging technologies in web intelligence. 2010 Aug 20;2(3):258-68.
2. Chauhan U, Tiwari T. Automatic Text Summarization and it’s Methods-a Survey.
3. Erkan G, Radev DR. Lexrank: Graph-based lexical centrality as salience in text summarization. Journal of artificial intelligence research. 2004 Dec 1;22:457-79.
4. S. M. R .. W. T. L., Brin, The page rank citation ranking: Bringing order to the web, Technical report, Stanford University, Stanford, CA., Tech. Rep., (1998).
5. Wang M, Wang X, Xu C. An approach to concept-obtained text summarization. InIEEE International Symposium on Communications and Information Technology, 2005. ISCIT 2005. 2005 Oct 12 (Vol. 2, pp.
1337-1340). IEEE.
6. Gong Y, Liu X. Generic text summarization using relevance measure and latent semantic analysis. InProceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval 2001 Sep 1 (pp. 19-25). ACM.
7. Ozsoy MG, Alpaslan FN, Cicekli I. Text summarization using latent semantic analysis. Journal of Information Science. 2011 Aug;37(4):405-17.
8. Hirao T, Nishino M, Yoshida Y, Suzuki J, Yasuda N, Nagata M. Summarizing a document by trimming the discourse tree. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP). 2015 Nov 1;23(11):2081-92.
9. Ramezani M, Feizi-Derakhshi MR. Ontology-Based Automatic Text Summarization Using FarsNet. Advances in Computer Science: an International Journal. 2015 Mar 31;4(2):88-96.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
