367,925 research outputs found
Siamese hierarchical attention networks for extractive summarization
[EN] In this paper, we present an extractive approach to document summarization based on Siamese Neural Networks. Specifically, we propose the use of Hierarchical Attention Networks to select the most relevant sentences of a text to make its summary. We train Siamese Neural Networks using document-summary pairs to determine whether the summary is appropriated for the document or not. By means of a sentence-level attention mechanism the most relevant sentences in the document can be identified. Hence, once the network is trained, it can be used to generate extractive summaries. The experimentation carried out using the CNN/DailyMail summarization corpus shows the adequacy of the proposal. In summary, we propose a novel end-to-end neural network to address extractive summarization as a binary classification problem which obtains promising results in-line with the state-of-the-art on the CNN/DailyMail corpus.This work has been partially supported by the Spanish MINECO and FEDER founds under project AMIC (TIN2017-85854-C4-2-R). Work of Jose-Angel Gonzalez is also financed by Universitat Politecnica de Valencia under grant PAID-01-17.González-Barba, JÁ.; Segarra Soriano, E.; García-Granada, F.; Sanchís Arnal, E.; Hurtado Oliver, LF. (2019). Siamese hierarchical attention networks for extractive summarization. Journal of Intelligent & Fuzzy Systems. 36(5):4599-4607. https://doi.org/10.3233/JIFS-179011S45994607365N. Begum , M. Fattah , and F. Ren . Automatic text summarization using support vector machine 5(7) (2009), 1987–1996.J. Cheng and M. Lapata . Neural summarization by extracting sentences and words. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers, 2016.K.M. Hermann , T. Kocisky , E. Grefenstette , L. Espeholt , W. Kay , M. Suleyman , and P. Blunsom . Teaching machines to read and comprehend, CoRR, abs/1506.03340, 2015.D.P. Kingma and J. Ba . Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2014.Lloret, E., & Palomar, M. (2011). Text summarisation in progress: a literature review. Artificial Intelligence Review, 37(1), 1-41. doi:10.1007/s10462-011-9216-zLouis, A., & Nenkova, A. (2013). Automatically Assessing Machine Summary Content Without a Gold Standard. Computational Linguistics, 39(2), 267-300. doi:10.1162/coli_a_00123Miao, Y., & Blunsom, P. (2016). Language as a Latent Variable: Discrete Generative Models for
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Noise tailoring, noise annealing and external noise injection strategies in memristive Hopfield neural networks
The commercial introduction of a novel electronic device is often preceded by
a lengthy material optimization phase devoted to the suppression of device
noise as much as possible. The emergence of novel computing architectures,
however, triggers a paradigm change in noise engineering, demonstrating that a
non-suppressed, but properly tailored noise can be harvested as a computational
resource in probabilistic computing schemes. Such strategy was recently
realized on the hardware level in memristive Hopfield neural networks
delivering fast and highly energy efficient optimization performance. Inspired
by these achievements we perform a thorough analysis of simulated memristive
Hopfield neural networks relying on realistic noise characteristics acquired on
various memristive devices. These characteristics highlight the possibility of
orders of magnitude variations in the noise level depending on the material
choice as well as on the resistance state (and the corresponding active region
volume) of the devices. Our simulations separate the effects of various device
non-idealities on the operation of the Hopfield neural network by investigating
the role of the programming accuracy, as well as the noise type and noise
amplitude of the ON and OFF states. Relying on these results we propose
optimized noise tailoring, noise annealing, and external noise injection
strategies.Comment: 13 pages, 7 figure
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