PaperRobot: Incremental Draft Generation of Scientific Ideas

By Qingyun Wang, Lifu Huang, Kevin Knight, Heng Ji, Mohit Bansal, Yi Luan. 2019

75 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology, Natural Language Processing

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Kai-Wei Chang

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Citations in the corpus

BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation (2021, background): “Natural language generation models are the central building blocks for many important artificial intelligence applications, including machine translation [17], text summarization [44], automatic storytelling [43], conversation bots [20], and writing assistants [39].”
A Survey of Knowledge-enhanced Text Generation (2020, unknown): “Followed by [180], Wang et al. proposed a novel system for scientific writing, called PaperRobot [147].”
AutoSurvey2: Empowering Researchers with Next Level Automated Literature Surveys (2025, unknown): “Building upon recent advances in automated literature review generation (Bosselut et al., 2018; Cho et al., 2018; Wang et al., 2019), the system integrates large language models (LLMs), semantic vector retrieval, and a modular graph-based execution mechanism to emulate the scholarly writing process…”
AutoSurvey: Large Language Models Can Automatically Write Surveys (2024, unknown): “PaperRobot [47] incrementally writes key elements to generate a paper abstract.”
Bloom-Eval: A Hierarchical Evaluation Benchmark for Automatic Survey Generation Based on Bloom's Taxonomy (2026, unknown): “The development of ASG is rooted in the broader landscape of scientific summarization (Hoang and Kan, 2010; Hu and Wan, 2014; Chen and Zhuge, 2019; Wang et al., 2019; Lu et al., 2020; Jin et al., 2020; Xing et al., 2020) and long-form text generation (Fan et al., 2018; Bosselut et al., 2018; Cho et…”