Abstract
Over the years as the technology advanced, the amount of data generated during the simulations and processing has been constantly increasing. Techniques for creating synopses of this massively generated data have been in the forefront of the research in the recent times. Text Summarization was one such aspect of the research which focused on representing the idea of the context in a short representation. Efforts were put to create a system which was able to generate effective summaries providing an overview of all the ideas represented by the article. Text Summarization techniques can be broadly classified into Extractive and Abstractive Text Summarization techniques. The paper compares all the prevailing systems, their shortcomings, and a combination of technologies used to achieve improved results. The paper also draws attention towards the state-of-the-art standardized datasets used in developing the summarization systems. The paper also focuses on testing parameters and techniques used to test the efficiency of the summarizing systems.
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Mahajani, A., Pandya, V., Maria, I., Sharma, D. (2019). A Comprehensive Survey on Extractive and Abstractive Techniques for Text Summarization. In: Hu, YC., Tiwari, S., Mishra, K., Trivedi, M. (eds) Ambient Communications and Computer Systems. Advances in Intelligent Systems and Computing, vol 904. Springer, Singapore. https://doi.org/10.1007/978-981-13-5934-7_31
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DOI: https://doi.org/10.1007/978-981-13-5934-7_31
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