Evidence plays a crucial role in any biomedical research narrative, providing
justification for some claims and refutation for others. We seek to build
models of scientific argument using information extraction methods from
full-text papers. We present the capability of automatically extracting text
fragments from primary research papers that describe the evidence presented in
that paper's figures, which arguably provides the raw material of any
scientific argument made within the paper. We apply richly contextualized deep
representation learning pre-trained on biomedical domain corpus to the analysis
of scientific discourse structures and the extraction of "evidence fragments"
(i.e., the text in the results section describing data presented in a specified
subfigure) from a set of biomedical experimental research articles. We first
demonstrate our state-of-the-art scientific discourse tagger on two scientific
discourse tagging datasets and its transferability to new datasets. We then
show the benefit of leveraging scientific discourse tags for downstream tasks
such as claim-extraction and evidence fragment detection. Our work demonstrates
the potential of using evidence fragments derived from figure spans for
improving the quality of scientific claims by cataloging, indexing and reusing
evidence fragments as independent documents.

通过从全文中提取文本片段，利用以生物医学为域的语料预训练的深度表征学习探讨科学论述结构的分析和证据片段的提取，以提高科学主张的质量。