2020
EMNLP
EMNLP 2020
AUTH @ CLSciSumm 20, LaySumm 20, LongSumm 20
Abstract
AbstractWe present the systems we submitted for the shared tasks of the Workshop on Scholarly Document Processing at EMNLP 2020. Our approaches to the tasks are focused on exploiting large Transformer models pre-trained on huge corpora and adapting them to the different shared tasks. For tasks 1A and 1B of CL-SciSumm we are using different variants of the BERT model to tackle the tasks of “cited text span” and “facet” identification. For the summarization tasks 2 of CL-SciSumm, LaySumm and LongSumm we make use of different variants of the PEGASUS model, with and without fine-tuning, adapted to the nuances of each one of those particular tasks.
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Interdisciplinary Bridge
— Deep Learning and Natural Language Processing
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Keyword Pioneer
— pegasus model
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
Authors
Topics
Deep Learning > Architectures > Transformers
Deep Learning > Techniques > Pretraining
Natural Language Processing > Generation > Summarization
Natural Language Processing > Resources & Methods > Large Language Models
Natural Language Processing > Applications > Summarization
Deep Learning > Models > Transformers
Deep Learning > Techniques > Fine-Tuning