@inproceedings{erera-etal-2019-summarization,
title = "A Summarization System for Scientific Documents",
author = "Erera, Shai and
Shmueli-Scheuer, Michal and
Feigenblat, Guy and
Peled Nakash, Ora and
Boni, Odellia and
Roitman, Haggai and
Cohen, Doron and
Weiner, Bar and
Mass, Yosi and
Rivlin, Or and
Lev, Guy and
Jerbi, Achiya and
Herzig, Jonathan and
Hou, Yufang and
Jochim, Charles and
Gleize, Martin and
Bonin, Francesca and
Bonin, Francesca and
Konopnicki, David",
editor = "Pad{\'o}, Sebastian and
Huang, Ruihong",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-3036",
doi = "10.18653/v1/D19-3036",
pages = "211--216",
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.",
}
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%0 Conference Proceedings
%T A Summarization System for Scientific Documents
%A Erera, Shai
%A Shmueli-Scheuer, Michal
%A Feigenblat, Guy
%A Peled Nakash, Ora
%A Boni, Odellia
%A Roitman, Haggai
%A Cohen, Doron
%A Weiner, Bar
%A Mass, Yosi
%A Rivlin, Or
%A Lev, Guy
%A Jerbi, Achiya
%A Herzig, Jonathan
%A Hou, Yufang
%A Jochim, Charles
%A Gleize, Martin
%A Bonin, Francesca
%A Konopnicki, David
%Y Padó, Sebastian
%Y Huang, Ruihong
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F erera-etal-2019-summarization
%X 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.
%R 10.18653/v1/D19-3036
%U https://aclanthology.org/D19-3036
%U https://doi.org/10.18653/v1/D19-3036
%P 211-216
Markdown (Informal)
[A Summarization System for Scientific Documents](https://aclanthology.org/D19-3036) (Erera et al., EMNLP-IJCNLP 2019)
ACL
- Shai Erera, Michal Shmueli-Scheuer, Guy Feigenblat, Ora Peled Nakash, Odellia Boni, Haggai Roitman, Doron Cohen, Bar Weiner, Yosi Mass, Or Rivlin, Guy Lev, Achiya Jerbi, Jonathan Herzig, Yufang Hou, Charles Jochim, Martin Gleize, Francesca Bonin, Francesca Bonin, and David Konopnicki. 2019. A Summarization System for Scientific Documents. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations, pages 211–216, Hong Kong, China. Association for Computational Linguistics.