@inproceedings{wang-etal-2019-paperrobot,
title = "{P}aper{R}obot: Incremental Draft Generation of Scientific Ideas",
author = "Wang, Qingyun and
Huang, Lifu and
Jiang, Zhiying and
Knight, Kevin and
Ji, Heng and
Bansal, Mohit and
Luan, Yi",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1191",
doi = "10.18653/v1/P19-1191",
pages = "1980--1991",
abstract = "We present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human-written papers in a target domain and constructing comprehensive background knowledge graphs (KGs); (2) creating new ideas by predicting links from the background KGs, by combining graph attention and contextual text attention; (3) incrementally writing some key elements of a new paper based on memory-attention networks: from the input title along with predicted related entities to generate a paper abstract, from the abstract to generate conclusion and future work, and finally from future work to generate a title for a follow-on paper. Turing Tests, where a biomedical domain expert is asked to compare a system output and a human-authored string, show PaperRobot generated abstracts, conclusion and future work sections, and new titles are chosen over human-written ones up to 30{\%}, 24{\%} and 12{\%} of the time, respectively.",
}
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<abstract>We present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human-written papers in a target domain and constructing comprehensive background knowledge graphs (KGs); (2) creating new ideas by predicting links from the background KGs, by combining graph attention and contextual text attention; (3) incrementally writing some key elements of a new paper based on memory-attention networks: from the input title along with predicted related entities to generate a paper abstract, from the abstract to generate conclusion and future work, and finally from future work to generate a title for a follow-on paper. Turing Tests, where a biomedical domain expert is asked to compare a system output and a human-authored string, show PaperRobot generated abstracts, conclusion and future work sections, and new titles are chosen over human-written ones up to 30%, 24% and 12% of the time, respectively.</abstract>
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%0 Conference Proceedings
%T PaperRobot: Incremental Draft Generation of Scientific Ideas
%A Wang, Qingyun
%A Huang, Lifu
%A Jiang, Zhiying
%A Knight, Kevin
%A Ji, Heng
%A Bansal, Mohit
%A Luan, Yi
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F wang-etal-2019-paperrobot
%X We present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human-written papers in a target domain and constructing comprehensive background knowledge graphs (KGs); (2) creating new ideas by predicting links from the background KGs, by combining graph attention and contextual text attention; (3) incrementally writing some key elements of a new paper based on memory-attention networks: from the input title along with predicted related entities to generate a paper abstract, from the abstract to generate conclusion and future work, and finally from future work to generate a title for a follow-on paper. Turing Tests, where a biomedical domain expert is asked to compare a system output and a human-authored string, show PaperRobot generated abstracts, conclusion and future work sections, and new titles are chosen over human-written ones up to 30%, 24% and 12% of the time, respectively.
%R 10.18653/v1/P19-1191
%U https://aclanthology.org/P19-1191
%U https://doi.org/10.18653/v1/P19-1191
%P 1980-1991
Markdown (Informal)
[PaperRobot: Incremental Draft Generation of Scientific Ideas](https://aclanthology.org/P19-1191) (Wang et al., ACL 2019)
ACL
- Qingyun Wang, Lifu Huang, Zhiying Jiang, Kevin Knight, Heng Ji, Mohit Bansal, and Yi Luan. 2019. PaperRobot: Incremental Draft Generation of Scientific Ideas. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1980–1991, Florence, Italy. Association for Computational Linguistics.