The Concept of System for Automated Scientific Literature Reviews Generation
[chapter]
Anton Teslyuk
2020
Lecture Notes in Computer Science
Key elements of the system include transformer-based BERT encoder, deep LSTM decoder and a loss function which combines autoencoder loss and forces generated summaries to be in the input text domain. ...
State of the art methods techniques based on generative adversarial networks (GANs), variational auto-encoders (VAE) and autoregressive models allow to generate images, videos, voice, texts which are very ...
The system is based on abstractive text summarization methods: autoencoder which combines transformer BERT encoder with LSTM decoder and additional loss factor which shapes latent space to be suitable ...
doi:10.1007/978-3-030-50420-5_32
fatcat:zcw6jsho5jfarc77mhhsxq6mku