Abstract
Space reduction is widely used in natural language processing tasks. In this paper, we use automatic text summarization as a use case to compare the quality of automatically generated summaries using two space reduction-based variants of the same summarization protocol. Obtained results show that non-linear space reduction-based summarization approaches outperform linear space-reduction-based ones. This research’s salient outcome is that it explains obtained results based on a rigorous study of the generated space sparsities
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Ben Ayed, A., Biskri, I., Meunier, JG. (2022). On Profiling Space Reduction Efficiency in Vector Space Modeling-Based Natural Language Processing. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 236. Springer, Singapore. https://doi.org/10.1007/978-981-16-2380-6_51
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