Abstract
Automatic text summarization is one of the mentionable research areas of natural language processing. The amount of data is increasing rapidly, and the necessity of understanding the gist of any text is just a mandatory tool, nowadays. The area of text summarization has been developing since many years. Mentionable research has been already done through extractive summarization approach; in other side, abstractive summarization approach is the way to summarize any text as like human. Machine will be able to provide a new type of summarization, where the understanding of given summary may found as like as human-generated summary. Several research developments have already been done for abstractive summarization in English language. This paper shows a necessary method—“text generation” in context of Bengali abstractive text summarization development. Text generation helps the machine to understand the pattern of human-written text and then produce the output as is human-written text. A basis recurrent neural network (RNN) has been applied for this text generation approach. The most applicable and successful RNN—long short-term memory (LSTM)—has been applied. Contextual tokens have been used for the better sequence prediction. The proposed method has been developed in the context of making it useable for further development of abstractive text summarization.
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Acknowledgements
We would like to thank our DIU-NLP and Machine Learning Research Lab for providing all research facility and guidance. We would also give special thanks to our Computer Science and Engineering department for supporting in completing our research.
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Abujar, S., Masum, A.K.M., Sanzidul Islam, M., Faisal, F., Hossain, S.A. (2020). A Bengali Text Generation Approach in Context of Abstractive Text Summarization Using RNN. In: Saini, H., Sayal, R., Buyya, R., Aliseri, G. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 103. Springer, Singapore. https://doi.org/10.1007/978-981-15-2043-3_55
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DOI: https://doi.org/10.1007/978-981-15-2043-3_55
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