Why Generative Adversarial Networks (GANs) is Challenging task in text summarization

Generative Adversarial Networks (GANs) can potentially be used in abstractive text summarization, although their application in this context is less common compared to other models like transformer-based seq2seq models. Abstractive text summarization involves generating a concise summary that may not be a verbatim extraction from the source text. GANs can be used in this task in the following ways:


1. Generator Network: In the context of text summarization, the generator network of a GAN can be designed to generate abstractive summaries. It takes in the source text as input and produces the summary as output. The generator network can be implemented using recurrent neural networks (RNNs), transformers, or other sequence-to-sequence models.


2. Discriminator Network: The discriminator network, which is a critical component of GANs, can be used to evaluate the quality and authenticity of the generated summaries. It assesses how well the generated summaries capture the essence of the source text. The discriminator can be a classifier that distinguishes between real human-generated summaries and those produced by the generator.


3. Adversarial Training: The generator and discriminator are trained adversarially. The generator aims to produce high-quality summaries that can fool the discriminator into classifying them as real. This adversarial training process encourages the generator to improve the quality of its generated summaries over time.


While GANs have been applied successfully in tasks such as image generation, their use in text summarization is less widespread. One challenge is the difficulty of defining a meaningful and effective discriminator for text. Additionally, training GANs for text generation tasks can be more challenging and less stable compared to other methods.


In practice, many abstractive text summarization models use sequence-to-sequence models with attention mechanisms, and these models have demonstrated strong performance in summarization tasks. However, researchers continue to explore the potential of GANs and other generative models for text summarization, and there may be innovations in the future that make GANs a more commonly used approach for this task.

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