Abstract
The presented paper proposes an extractive text summarization technique for single documents using Neural Networks and Genetic Algorithms. The Neural Network helps to define a fitness function to express mathematically the quality of the generated summary through six desired properties which are theme similarity, cohesion, sentiment, readability, aggregate similarity and sentence position. Genetic Algorithm maximizes the above-mentioned fitness function, and extracts the most important sentences to create the extractive summary. The results are compiled using DUC2002 data as a benchmark and calculated using the precision-recall technique. They are compared with techniques using Genetic Algorithm, Neural Network and a summarizer made by Microsoft. The comparison between the results clearly demonstrates the superiority of the technique and is very encouraging for future work in this area.
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A Appendix
A Appendix
Graph comparing the results of all the different techniques.
Document No. 17 of the DUC 2002 data set containing 28 sentences.
Ideal Summary for Document No.17, containing sentences 1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 14, 15, 17, 18, 23, 24.
Summary for Document No.17 generated by our algorithm, containing sentences 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 16, 17, 18, 25, 26. Precision = 0.6875.
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Chatterjee, N., Jain, G., Bajwa, G.S. (2019). Single Document Extractive Text Summarization Using Neural Networks and Genetic Algorithm. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 858. Springer, Cham. https://doi.org/10.1007/978-3-030-01174-1_26
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DOI: https://doi.org/10.1007/978-3-030-01174-1_26
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