2019
EMNLP
EMNLP 2019
A Summarization System for Scientific Documents
Abstract
AbstractWe present a novel system providing summaries for Computer Science publications. Through a qualitative user study, we identified the most valuable scenarios for discovery, exploration and understanding of scientific documents. Based on these findings, we built a system that retrieves and summarizes scientific documents for a given information need, either in form of a free-text query or by choosing categorized values such as scientific tasks, datasets and more. Our system ingested 270,000 papers, and its summarization module aims to generate concise yet detailed summaries. We validated our approach with human experts.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Natural Language Processing
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Keyword Pioneer
— scientific summarization
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Hot Topic Early Bird
— document summarization
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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, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Foundation Models
Machine Learning > Learning Types > Semi-Supervised Learning
Machine Learning > Application Areas > Data Augmentation
Machine Learning > Application Areas > Domain Adaptation
Natural Language Processing > Applications > Information Retrieval
Natural Language Processing > Applications > Summarization