Abstract
The domain of summarization of multimedia is rapidly evolving and creating an impact in the present world in several verticals. An enormous amount of hardware resources are involved in the communication ecosystem. The scalable nature along with diversity and specificity is a major challenge in establishing effective summarization algorithms. The present algorithms are incapable of capturing semantic relationships among words as they focus majorly upon the extractive version of summarization. The analysis focuses entirely upon abstractive summarization of textual corpus. The system incorporates heuristic approaches to train the system and predict test data. The entire model is developed using state-of-the-art architectures by incorporating the Transformer model. The encoder and decoder schemes help in effective summaries as compared to the extractive approach.
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Ghadekar, P., Anand, D.S., Gupta, A.K., Oswal, P., Sharma, D., Khare, S. (2023). Audio Based Text Summarization Using Natural Language Processing. In: Chakraborty, B., Biswas, A., Chakrabarti, A. (eds) Advances in Data Science and Computing Technologies. ADSC 2022. Lecture Notes in Electrical Engineering, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-99-3656-4_17
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DOI: https://doi.org/10.1007/978-981-99-3656-4_17
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