分类:PaperRobot: Incremental Draft Generation of Scientific Ideas

Qingyun Wang, Lifu Huang, Zhiying Jiang, Kevin Knight, Heng Ji, Mohit Bansal, Yi Luan, PaperRobot: Incremental Draft Generation of Scientific Ideas, arXiv:1905.07870

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.

总结和评论

这篇文章^[1]实现了一个能够提出来研究论文的idea的机器人，并且还可以产生论文的一部分（背景介绍、问题定位）。其主要思路是：从现有论文和知识库中体现出来概念地图或者说知识地图——其主要包含概念和概念关系；然后，从概念地图中做链路预测，找到可能可以进一步研究的概念之间的联系。最后，按照这个联系还有概念地图、知识背景来创作文章，把真正的研究部分交给研究者来完成。

其中，最重要的技术就是概念和概念之间关系的提取。

进一步研究

选择任何一个现象，以及和这个现象相关的一组词，都可以用词频时间序列来看一下这个现象的时间演化。随着word2vec技术的发展，我们甚至可以从这一组词出发做细分做扩张、研究最相近的其他词，来做更好的词频统计。

参考文献