A Summarization System for Scientific Documents
Computation and Language
2019-08-30 v1
Abstract
We 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.
Cite
@article{arxiv.1908.11152,
title = {A Summarization System for Scientific Documents},
author = {Shai Erera and Michal Shmueli-Scheuer and Guy Feigenblat and Ora Peled Nakash and Odellia Boni and Haggai Roitman and Doron Cohen and Bar Weiner and Yosi Mass and Or Rivlin and Guy Lev and Achiya Jerbi and Jonathan Herzig and Yufang Hou and Charles Jochim and Martin Gleize and Francesca Bonin and David Konopnicki},
journal= {arXiv preprint arXiv:1908.11152},
year = {2019}
}
Comments
Accepted to EMNLP 2019