PaperRobot: Incremental Draft Generation of Scientific Ideas

Wang, Qingyun; Huang, Lifu; Jiang, Zhiying; Knight, Kevin; Ji, Heng; Bansal, Mohit; Luan, Yi

doi:10.18653/v1/p19-1191

Cited by 31 publications

(24 citation statements)

References 51 publications

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“…Describing their system called PaperRobot, built prior to the pandemic outbreak, Wang et al describe that "creating new nodes usually means discovering new entities (e.g. new proteins) through a series of laboratory experiments, which is probably too difficult for PaperRobot" [55].…”

Section: Knowledge Graph Constructionmentioning

confidence: 99%

Deep Learning applications for COVID-19

2021

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This survey explores how Deep Learning has battled the COVID-19 pandemic and provides directions for future research on COVID-19. We cover Deep Learning applications in Natural Language Processing, Computer Vision, Life Sciences, and Epidemiology. We describe how each of these applications vary with the availability of big data and how learning tasks are constructed. We begin by evaluating the current state of Deep Learning and conclude with key limitations of Deep Learning for COVID-19 applications. These limitations include Interpretability, Generalization Metrics, Learning from Limited Labeled Data, and Data Privacy. Natural Language Processing applications include mining COVID-19 research for Information Retrieval and Question Answering, as well as Misinformation Detection, and Public Sentiment Analysis. Computer Vision applications cover Medical Image Analysis, Ambient Intelligence, and Vision-based Robotics. Within Life Sciences, our survey looks at how Deep Learning can be applied to Precision Diagnostics, Protein Structure Prediction, and Drug Repurposing. Deep Learning has additionally been utilized in Spread Forecasting for Epidemiology. Our literature review has found many examples of Deep Learning systems to fight COVID-19. We hope that this survey will help accelerate the use of Deep Learning for COVID-19 research.

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Section: Knowledge Graph Constructionmentioning

confidence: 99%

Deep Learning applications for COVID-19

2021

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“…Text generation in the scientific domain has achieved progress in several ways. Wang et al (2019) generates the paper abstract from the input title along with predicted entities in the related papers and further generates the paragraphs for the conclusion and future work. Demir et al (2019) generates the LATEX source code with a sequenceto-sequence model in a straightforward manner.…”

Section: Text Generation In Scientific Domainmentioning

confidence: 99%

“…To enable the model to generate plausible paragraphs, we introduce context-aware paragraph generation, which is a task that aims to generate paragraphs for the "Introduction" section. Unlike previous works, which generates paragraph using limited information (e.g., abstract) (Wang et al, 2019;Demir et al, 2019;, we provide the model with substantial contextual information C, which are the body texts in the cited papers. For simplicity, we only use the cited papers involved in the "Introduction" section and ignore the objects in them.…”

Section: Context-aware Paragraph Generationmentioning

confidence: 99%

SciXGen: A Scientific Paper Dataset for Context-Aware Text Generation

Hong¹,

Takamura²,

Nakayama³

2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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Generating texts in scientific papers requires not only capturing the content contained within the given input but also frequently acquiring the external information called context. We push forward the scientific text generation by proposing a new task, namely context-aware text generation in the scientific domain, aiming at exploiting the contributions of context in generated texts. To this end, we present a novel challenging largescale Scientific Paper Dataset for ConteXt-Aware Text Generation (SciXGen), consisting of well-annotated 205,304 papers with full references to widely-used objects (e.g., tables, figures, algorithms) in a paper. We comprehensively benchmark, using state-of-the-arts, the efficacy of our newly constructed SciX-Gen dataset in generating description and paragraph. Our dataset and benchmarks will be made publicly available to hopefully facilitate the scientific text generation research.

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“…With drug repurposing as a case study, we focus on 11 typical questions that human experts pose and integrate our techniques to generate a comprehensive report for each candidate drug. Our coarse-grained Information Extraction (IE) system consists of three components: (1) coarsegrained entity extraction (Wang et al, 2019a) and entity linking (Zheng et al, 2015) for four entity types: Gene nodes, Disease nodes, Chemical nodes, and Organism. We follow the entity ontology defined in the Comparative Toxicogenomics Database (CTD) (Davis et al, 2016), and obtain a Medical Subject Headings (MeSH) Unique ID for each mention.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation

Wang¹,

Li²,

Wang³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract finegrained multimedia knowledge elements (entities and their visual chemical structures, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence. All of the data, KGs, reports 1 , resources, and shared services are publicly available 2 .

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PaperRobot: Incremental Draft Generation of Scientific Ideas

Cited by 31 publications

References 51 publications

Deep Learning applications for COVID-19

Deep Learning applications for COVID-19

SciXGen: A Scientific Paper Dataset for Context-Aware Text Generation

COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation

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