Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks (D18-1)
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| Challenge: | a popular approach to learning data representations involves the use of an auto-encoder that compresses data into a latent-space representation without supervision. |
| Approach: | They propose to train an auto-encoder that encodes input text into human-readable sentences . they use comprehensible natural language as a latent representation of the input source text . |
| Outcome: | The proposed auto-encoder can encode input text into human-readable sentences without document-summary pairs. |
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Vidhisha Balachandran, Artidoro Pagnoni, Jay Yoon Lee, Dheeraj Rajagopal, Jaime Carbonell, Yulia Tsvetkov
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Dmitrii Aksenov, Julian Moreno-Schneider, Peter Bourgonje, Robert Schwarzenberg, Leonhard Hennig, Georg Rehm
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