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Learning to Encode Text as Human Readable Summaries using Generative Adversarial Networks

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Figure

Figure 1: Proposed model. Given long text, thegenerator produces a shorter text as a summary.The generator is learned by minimizing the recon-struction loss together with the reconstructor andmaking discriminator regard its output as human-written text.
Figure 2: Architecture of proposed model.
Figure 3: When the second arrested appears, as thesentence becomes ungrammatical, the discrimina-tor determines that this example comes from thegenerator
Table 1: Average F1 ROUGE scores on English Gigaword. R-1, R-2 and R-L refers to ROUGE 1,ROUGE 2 and ROUGE L respectively
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