Scientific Discourse Tagging for Evidence Extraction (2021)

Abstract: 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.

## introduction primary experimental articles (i.e., papers that describe original experimental work) provide the crucial raw material for all other subsequent scientific research. however, the drastically growing number of scientific literature makes it increasingly difficult for domain experts to efficiently utilize them. automatic information extraction from biomedical * work performed while the author is interning at the information sciences institute, university of southern california lc3 , the mammalian atg8 homolog , undergoes a set of modifications resulting in conversion from lc3i to lc3ii during autophagy 42 . extracting important information from biomedical literature to facilitate and accelerate scientific discovery has been a goal for computational linguistics for some time @xcite , with the focus of identifying relevant entities, relations, and events from text to populate a knowledge base. however, these methods do not take into account the fact that scientific work involves attempting to provide explanations for evidence derived from experiments and is therefore driven principally by authors attempting to convince expert readers that their claims are the "correct" explanations for the experimental evidence. thus, an important aspect of building machines capable of understanding scientific literature is first recognizing different rhetorical components of scientific discourse, with which we will then be able to distinguish the observations made in experiments from their implications and distinguish between claims supported by evidence and hypotheses put forward to prompt further research. it is this goal, of being able to distinguish between the different rhetorical components of scientific discourses so that we can build ai systems to facilitate more accurate analysis and understand- ## experimental setup we evaluate the performance of our scientific discourse tagger on pubmed-rct dataset (dernoncourt and @xcite and scidt dataset @xcite (section 5.1). we also examine the transferablity of our scientific discourse tagger to new datasets using coda-19 dataset @xcite ) (section 5.2). we further study the efficiency of scientific discourse tags on claim-extraction task via transfer learning as well as evidence fragment detection task in a pipeline fashion (section 6). ## discussion we use the claim-extraction task and the evidence fragment detection task as two examples to demonstrate the benefit of leveraging pre-trained scientific discourse taggers and scientific discourse tags to improve the downstream-task performance via transfer learning or in a pipeline fashion. as burns et al. (2017) proposed, given the output of evidence fragment detection system, we can link subfigure codes with intact @xcite records to obtain evidence fragments for each experimental figure. we further suggest that the evidence fragment detection task can help biocurators delineate evidence fragments as independent documents so they can be cataloged, indexed, and reused. traditionally scientists' arguments are based on relationships between claims and evidences within the same paper and possibly a limited number of cited papers. with the help of evidence fragments, we are able to discard the convention of only linking claims to evidence from a single paper or of following citations, which are often based on linking separate claims from different papers. as a future work, we can surface the evidence fragments combined with figures and captions across multiple papers. @xcite proposed the "micropublications" semantic model, which is an abstract framework that integrates scientific argument and evidence from scientific documents. our scientific discourse tagger, claim extractor and evidence fragment detector may serve as the actual implementation of the modules in such a framework. ultimately, we hope to dramatically increase the amount of primary evidence used to generate individual claims and therefore improve the quality of those claims. ## conclusions we develop a state-of-the-art model for scientific discourse tagging and demonstrate its strong performance on pubmed-rct dataset @xcite and scidt dataset @xcite as well as its strong transferability on new datasets such as coda-19 dataset @xcite . we then demonstrate the benefit of leveraging the scientific discourse tags on downstream-tasks by providing claim-extraction task and evidence fragment detection task as two show cases. we further propose a future direction that scientific discourse tagging helps delineate evidence fragments as independent documents so they can be cataloged, indexed, and reused. as a result, we can dramatically increase the amount of primary evidence used to generate individual claims and therefore improve the quality of those claims.