Deep Learning for Natural Language Processing: Novel State-of-the-art Solutions in Summarisation of Legal Case Reports

Deep neural networks are one of the major classification machines in machine learning. Several Deep Neural Networks (DNNs) have been developed and evaluated in recent years in recognition tasks, such as estimating a user base and estimating their interactivity. We present the best algorithms for extracting or summarising text using a deep neural network while allowing the workers to interpret the texts from the output speech. In this work both extractive and abstractive summarisation approaches have been applied. In particular, BERT (Base, Multilingual Cased) and a deep neural network composed by CNN and GRU layers have been used in the extraction-based summarisation while the abstraction-based one has been performed by applying the GPT-2 Transformer model. We show our models achieve high scores in syntactical terms while a human evaluation is still needed to judge the coherence, consistency and unreferenced harmonicity of speech. Our proposed work outperform the state of the art results for extractive summarisation on the Australian Legal Case Report Dataset. Our paper can be viewed as further demonstrating that our model can outperform the state of the art on a variety of extractive and abstractive summarisation tasks. Note: The abstract above was not written by the author, it was generated by providing a part of thesis introduction as input text to the pre-trained GPT-2 (Small) Transformer model used in this work which has been previously fine-tuned for 4 epochs with the”NIPS 2015 Papers” dataset.