Enhancing Pre-trained Language Representation for Multi-Task Learning of Scientific Summarization

This paper aims to extract summarization and keywords from scientific articles simultaneously, while abstract extraction (AE) and key extraction (KE) are considered as auxiliary tasks to each other. For the data scarcity in scientific AE and KE tasks, we propose a multi-task learning framework which uses huge unlabeled data to learn scientific language representation (pre-training) and uses smaller annotated data to transfer the learned representation to AE and KE (fine-tuning). Although the pre-trained language model performs well in universal natural language tasks, its capacity still has a margin of improvement for specific tasks. Inspired by this intuition, we use another two tasks keyword masking and key sentence prediction before the fine-tuning phase to enhance the language representation for AE and KE. This language representation enhancing stage uses the same labeled data but different optimization objectives with the fine-tuning phase. In order to evaluate our model, we develop and release a high-quality annotated corpus for scientific papers with keywords and abstract. We conduct comparative experiments on this dataset, and experimental results show that our multi-task learning framework achieves the state-of-the-art performance, proving the effectiveness of the language model enhancing mechanism.