Pathway2Text: Dataset and Method for Biomedical Pathway Description GenerationDownload PDF

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08 Mar 2022, 17:01 (modified: 05 May 2022, 16:43)NAACL 2022 Conference Blind SubmissionReaders: Everyone
Abstract: Biomedical pathways have been extensively used to characterize the mechanism of complex diseases. One essential step in biomedical pathway analysis is to curate the description of a pathway based on its graph structure and node features. Neural text generation could be a plausible technique to circumvent the tedious manual curation. In this paper, we propose a new dataset Pathway2Text, which contains 2,367 pairs of biomedical pathways and textual descriptions. All pathway graphs are experimentally derived or manually curated. All textual descriptions are written by domain experts. We form this problem as a Graph2Text task and propose a novel graph-based text generation approach $k$NN-Graph2Text, which explicitly exploited descriptions of similar graphs to generate new descriptions. We observed substantial improvement of our method on both Graph2Text and the reverse task of Text2Graph. We further illustrated how our dataset can be used as a novel benchmark for biomedical named entity recognition. Collectively, we envision our method will become an important benchmark for evaluating Graph2Text methods and advance biomedical research for complex diseases.
Paper Link: https://openreview.net/forum?id=NZ-yd023Fey
Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)
Presentation Mode: This paper will be presented virtually
Virtual Presentation Timezone: UTC+8
Copyright Consent Signature (type Name Or NA If Not Transferrable): Junwei Yang
Copyright Consent Name And Address: School of EECS, Peking University, Beijing, China
Dataset: zip
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