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2020 EMNLP EMNLP 2020

Dimsum @LaySumm 20

Abstract

AbstractLay summarization aims to generate lay summaries of scientific papers automatically. It is an essential task that can increase the relevance of science for all of society. In this paper, we build a lay summary generation system based on BART model. We leverage sentence labels as extra supervision signals to improve the performance of lay summarization. In the CL-LaySumm 2020 shared task, our model achieves 46.00 Rouge1-F1 score.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — lay summarization
🐝 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

Tiezheng Yu , Dan Su , Wenliang Dai , Pascale Fung

Topics

Machine Learning > Core Methods > Representation Learning Natural Language Processing > Applications > Summarization Deep Learning > Models > Transformers Deep Learning > Learning Types > Sequence Modeling

Keywords

sequence labeling extractive summarization text generation abstractive summarization lay summarization rouge metric bart model sentence label
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