Abstract
Due to the availability of huge data on the Internet, it becomes a time-consuming process for the user to find the most relevant text related to their interesting topic. This process can be simplified by text summarization. Initially, a set of multiple documents are preprocessed separately. Later, word frequency and the similarity of the sentences from every preprocessed document are calculated and then most redundant sentences are eliminated. Next, the content from these multiple preprocessed document is merged into a single input document. Finally, the merged document is given as input to the proposed Binary Fruit Fly Optimization Algorithm to generate a text summary. We used the ROUGE system to evaluate the results.
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Mamidala, K.K., Sanampudi, S.K. (2021). Text Summarization of Multiple Documents Using Binary Fruit Fly Optimization Algorithm. In: Jyothi, S., Mamatha, D.M., Zhang, YD., Raju, K.S. (eds) Proceedings of the 2nd International Conference on Computational and Bio Engineering . Lecture Notes in Networks and Systems, vol 215. Springer, Singapore. https://doi.org/10.1007/978-981-16-1941-0_78
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DOI: https://doi.org/10.1007/978-981-16-1941-0_78
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