Argument Mining for Scholarly Document Processing: Taking Stock and Looking Ahead (2021)

Abstract: Argument mining targets structures in natural language related to interpretation and persuasion which are central to scientific communication. Most scholarly discourse involves interpreting experimental evidence and attempting to persuade other scientists to adopt the same conclusions. While various argument mining studies have addressed student essays and news articles, those that target scientific discourse are still scarce. This paper surveys existing work in argument mining of scholarly discourse, and provides an overview of current models, data, tasks, and applications. We identify a number of key challenges confronting argument mining in the scientific domain, and suggest some possible solutions and future directions.

## introduction scientific papers aim to present verifiable evidence for a series of stated claims, anchoring these claims in experiments, data, and references. however, the interpretation of such objective sources of evidence is often ambiguous and subjective. thus, much of scientific communication is essentially persuasive and uses an argumentative structure to establish the relevance, validity, and novelty of an author's main claims and conclusions @xcite . this argumentation takes the form of a dialogue between the author and her readers, in which new knowledge is proposed and an attempt made to persuade the readers to accept and follow particular claims @xcite . however, most current research on automatic document processing ignores this argumentative context and treats statements that are persuasive, tentative, or speculative to be factual. this risks overstating the certainty of claims and hypotheses, and bypasses *these authors contributed equally the rhetorical aspect of scientific discourse (see e.g. @xcite ). computational argumentation is a recent and growing field of research concerned with the computational analysis and generation of natural language arguments and argumentative discourses. over the past decade, this area has attracted researchers seeking to tackle different tasks including argument mining, argument quality assessment, and argument generation (for an overview, see e.g. @xcite ). the most studied task is argument mining, i.e., the identification of argumentative units, argument components (e.g., conclusion and premise), and structures of text documents. however, despite a wealth of natural language processing (nlp) research on extracting information from scientific literature-including entity extraction @xcite , relation identification @xcite , question answering @xcite , and summarization @xcite -relatively few attempts have been made to model argumentative structures in science. this paper argues for an increased focus of the nlp community on argument mining in scientific documents. to encourage work at the intersection of scholarly discourse processing and argument mining, we provide a brief overview of current work in this field, and discusses the most used models, data, methods, and applications. we discuss a number of challenges in mining the argumentative structure of scientific documents and propose some promising future directions. ## argumentation in scientific discourses to support future efforts on argument mining of scientific documents, we present a survey of the literature from 2000 to the present, summarized in for each paper, we identified the domain of study (i.e., a specific scientific domain, full-text or abstracts), the objectives of the work, and the methods used. furthermore, the papers can be categorized under four areas of study, discussed, in turn, below. ## challenges in this section, we describe a few challenges that are relevant to argument mining in the scientific literature. although not only specific for the scientific domain, these are hurdles that need to be faced in future research to allow progress to be made. ## discussion in summary, we have provided a brief overview of current work and a summary of issues that need to be addressed to make headway in the automated argument mining for scholarly documents. we hope to have shown that more research is needed in this field to enable better representation of the persuasive aspects of scholarly communication. this can help provide a more realistic representation of how scientific knowledge is obtained, and how authors aim to persuade readers of the validity of claims. in particular, seeing scholarly discourse as a pragmatic discourse, i.e. one that humans undertake with interpersonal, as well as informative goals, can allow richer representations of the knowledge structures underlying scientific progress. as noted, applications of argument mining in scientific discourse, such as summarization and aids to technical writing, to date have been limited to those that are relatively robust to errors, a partial consequence of the immaturity of the field. in particular, these applications are mostly insensitive to the factual content of scientific arguments. meanwhile, a relatively mature community continues to expand models and methods for information extraction in various scientific domains, usually with no attention to the argumentative context in which the target facts are presented. because a correct understanding and use of facts is critical to scientific understanding and progress, we see an opportunity for many innovative applications at the intersection of fact and argument. for example, models capable of determining the salience of individual facts in a domain could provide the basis for highly precise forms of scientific information retrieval, or even offer forms of automation that assist scientists in maximizing the pertinence of their experiments. to achieve this vision at scale, the argument mining community must grapple with the problem of increasing scientific domain specialization. it is crucial that we separate the invariant features of scientific argumentation from those that vary with field and specialization, and that we investigate effective methods of cross-domain transfer. to this end, the field should seek consensus regarding how scientific argumentation should be formalized and strive for broad-coverage reference corpora annotated under guidelines optimized for high interannotator agreement. to support these efforts, we suggest a greater collaboration between participants of the scholarly document processing and argument mining domains, with a particular focus on creating shared models and shared and accessible corpora to spur on research. we hope such conversations can commence at this workshop and others, to inspire and unite members of both communities with natural language processing and improve sharing and improving the outputs of science and scholarship. ## conclusion this paper endeavors at promoting the collaboration between the communities of scholarly discourse processing and computational argumentation, arguing for the ultimate importance of more extensive research on argument mining in scientific documents. particularly, we address the current contributions on argument mining for scientific documents by surveying about 40 papers that approach different aspects and tasks such as proposing annotation schemes, creating corpora, and identifying argumentative discourse units as well as argumentative relations in scientific documents. furthermore, we describe various challenges for mining argumentative structures of scientific documents and suggest some strategic directions in order to accomplish remarkable benefits on a wide range of downstream applications such as scientific writing assistance, scientific articles summarization, and quality assessment. anita de waard, s buckingham shum, annamaria carusi, jack park, matthias samwald, and ágnes sándor. 2009. hypotheses, evidence and relationships: the hyper approach for representing scientific knowledge claims. douglas walton, chris reed, and fabrizio macagno. 2008. argumentation schemes. cambridge university press. mark weinstein. 1990. towards an account of argumentation in science. argumentation, 4(3):269-298. an yang and sujian li. 2018. scidtb: discourse dependency treebank for scientific abstracts. in proceedings of the 56th annual meeting of the association for computational linguistics (volume 2: short papers), pages 444-449, melbourne, australia. association for computational linguistics. antonio jimeno yepes, james g mork, and alan r aronson. 2013. using the argumentative structure of scientific literature to improve information access. in proceedings of the 2013 workshop on biomedical natural language processing, pages 102-110. bei yu, jun wang, lu guo, and yingya li. 2020. measuring correlation-to-causation exaggeration in press releases. in proceedings of the 28th international conference on computational linguistics, pages 4860-4872, barcelona, spain (online). international committee on computational linguistics.