B io C ite: A Deep Learning-based Citation Linkage Framework for Biomedical Research Articles (2022)

Abstract: Research papers reflect scientific advances. Citations are widely used in research publications to support the new findings and show their benefits, while also regulating the information flow to make the contents clearer for the audience. A citation in a research article refers to the information’s source, but not the specific text span from that source article. In biomedical research articles, this task is challenging as the same chemical or biological component can be represented in multiple ways in different papers from various domains. This paper suggests a mechanism for linking citing sentences in a publication with cited sentences in referenced sources. The framework presented here pairs the citing sentence with all of the sentences in the reference text, and then tries to retrieve the semantically equivalent pairs. These semantically related sentences from the reference paper are chosen as the cited statements. This effort involves designing a citation linkage framework utilizing sequential and tree-structured siamese deep learning models. This paper also provides a method to create a synthetic corpus for such a task.

## introduction research articles from different domains use varying writing styles and formats. they serve different purposes as well. a research publication may discuss current research trends, a novel discovery, or alternative approaches to solving a problem in a given domain. while writing a research article, the author mentions prior research that was either significant in resolving the same topic or impacted the author's views mentioned in the current research paper. this referencing another document in a research piece is referred to as a citation @xcite . this way, citations establish connections between distinct research literature as well as alleviating authors' writing burden by preventing them from having to write the same thing mentioned in another research article again. simultaneously, it assists readers in acquiring prior knowledge about a subject that may be necessary to comprehend the ideas contained in the ongoing research work. the idea of citation indexing was first introduced in 1964 where indexes contain the references in a research document. citation-based bibliometrics are utilized to evaluate the significance of a research work @xcite . in response to the growing popularity of citation indexing, a more critical analysis of citing was later suggested. garzone and mercer @xcite devised a mechanism for determining the objective of a reference in biochemistry and physics research publications. moreover, citations help to keep track of the logical argumentation across various research articles @xcite . prominent applications of citation incorporate maintaining the trail of scientific research argumentation across different research articles @xcite and summarization of these documents @xcite . in scientific research publications, a citation refers to the source article from which the cited notion is drawn. however, in experimental biomedical research articles, a citing sentence usually only relates to a small text span of the cited document's contents. this small span of text can be from the method section, result analysis section or any other section of the reference document @xcite . the above-mentioned applications would substantially benefit if such a text span could be extracted from the original document. it would also free up the readers from having to read the full document to locate the cited piece of text. the citation linkage task is more complicated for biomedical research papers as the same chemical or biological component has various representation formats and the use of these variations is very common in such research articles. for example, the chemical compound carbon dioxide can be represented as co 2 as well as @xmath0, whereas in some articles the writers write the whole name in plain text (carbon dioxide). similarly, there are multiple representations to indicate the same reactions between various genes, chemicals, and drugs. on top of that, the only human annotated corpus available for the citation linkage task in the biomedical domain is from @xcite which comes with 3857 sentence pairs which are highly imbalanced with only 2% positive samples and 98% negative samples. the size and imbalanced nature of this corpus makes it difficult to train deep learning models on this dataset. to overcome this, we propose an automatically generated corpus for this task containing 74,568 sentence pairs. this paper has two objectives: first, introducing an automatically generated corpus for the citation linkage task for biomedical research papers and second, providing a framework for this task to retrieve the cited text span from the reference paper given the citing sentence by means of measuring the semantic similarities between the citing sentence and candidate cited sentences from the referenced paper. the cited text span can be a single sentence, part of a sentence or even one or more paragraphs @xcite . however, for this task this text span is restricted to a single sentence like @xcite . considering the first objective, we introduce an automatically generated corpus containing 74,568 sentence pairs and also an approach to annotate data automatically without any human effort. the quality of the data annotation is evaluated by annotating a portion of the dataset by human experts and then measuring cohen's κ among the human annotators' decisions and the automatically generated annotation labels. sentence pairs from this dataset are used only for training the models for the citation linkage task. and for the second aspect, we have investigated multiple sequential and tree-structured neural networks and presented one ensemble architecture, which we call biocite, that computes the semantic similarity between the citing statement and all of the sentences in the referred document. the performance of the model is tested against the expert annotated dataset from @xcite which contains citing sentences that refer to methods statements in the cited documents. the outline for the paper is: section 2 gives a brief description of the citation linkage task and section 3 mentions and discusses a few prominent works for the citation linkage task. section 4 discusses the automatically generated corpus creation and the framework design. the performance of the models are reported and analyzed in section 5. the parameters of the models are also described in this section. the paper ends with a brief summary and possible future directions of this research. ## citation linkage citations construct semantic bridges between citing and cited manuscripts. to support the findings, claims and hypotheses, authors cite several resources while preparing manuscripts. they also try to address the results and findings of the other research works. it is also important to mention others' works, in order to demonstrate the authors' significance and progress with their current work. a citation in any research paper focuses on some specific sections of the referenced article acknowledged as the citation context. this citation context often focuses on a specific idea or issue in the referenced manuscript @xcite . the intent of a using citation is to provide the readers with the apposite background information for a better understanding of the concepts introduced in the citing paper. the citation context can reveal information about a cited publication's hypotheses, findings, methodologies, etc. in order to improve the performance or make the method compatible with the domain for which it is intended to be used, an author may adapt or modify the method described in the citing paper to the extent necessary. aside from that, the author may undertake experiments based on the idea presented in a cited paper to confirm or refute the idea presented in that work. references to the hypotheses and methodologies that were employed in the referenced paper aid the readers to grasp the concepts presented in the current work. however, citations only provide the source of information which is being referred. the current citation indexing approach does not provide a way to indicate which text span from the cited research manuscript is actually being touched on. it provides no method other than going through the whole referenced article for the reader if he or she wants to grasp the idea properly. on the other hand, research articles that include detailed information on the study's discoveries, as well as relevant background information, are more appealing to readers. this necessity has influenced the work we are presenting in this paper. the author can cite a paper by paraphrasing the statements from the cited paper. he or she can also elaborate some statements from the cited paper. for example in the citing statement, "dna samples are frequently harmed by exposure to excessively acidic environment", @xcite explains that "ph4" is an "excessively acidic environment" when citing "dna is fairly stable in mildly acidic solutions, although the beta glycosidic link in the purine bases is hydrolyzed at around ph4." @xcite . sometimes these citations are the interpretations of the cited statements, e.g., the citing sentence "different pcr buffer systems and/or taq polymerases may produce variable results in real time pcr." @xcite is nothing but an interpretation of the cited sentence "there is a significant disparity between the outcomes obtained using the various dna polymerase-buffer solutions." @xcite . as these examples demonstrate, precise mapping between words and sentences is required to establish a connection between the citing and cited sentences. this paper provides a citation linkage framework for biomedical research articles along with an automatically generated corpus comprising 74,568 sentence pairs. the framework at first generates sentence pairs with the citing sentence and all the sentences from the referenced paper. then, the model measures the semantic similarity scores between the sentences in each pair. based on these similarity scores, it retrieves the actual cited sentences from the referenced manuscript. we have formulated this semantic similarity measurement task as a binary classification task where each sentence pair is predicted with either label 1 or label 0. sentence pairs predicted with label 1 are selected as the cited sentences given the particular citing sentence. ## related work the study of citations in scientific research has led to a lot of work. citation analysis attempts to identify which section (i.e., abstract of the paper, introduction of the problem statement, description of methods, analysis of result, etc.) of the referenced article this sentence refers to @xcite . however, this form of study cannot pinpoint the citation span. another type of work is to determine the citation span. polyu @xcite applied ranksvm over chunks of sentences to predict the cited text span. @xcite computed cosine similarity of word embeddings for the citation linkage task. @xcite applied majority voting to six machine learning classifiers over the lexical, knowledge-based, corpus-based, syntactic and surface features for this task. the cl-scisumm shared task tries to solve three aspects: find the cited text span given the citation sentence ("citance"), identifying the discourse facet of the cited sentence and summarise the referred article using only the text spans that are quoted many times in the referenced document. however, the later two sub-tasks are out of the scope of this work. @xcite applied different classifiers and voting mechanism over similarity, rule and position-based features to determine the similarity between the citing and cited statements for cl-scisumm-17. the citation linkage between citing and cited sentence pairs was determined by @xcite utilizing inverse document frequency and jaccard similarity. in their following works, they computed the sentence vectors by concatenating 200 dimensional word vectors @xcite and then applying a convolutional neural network (cnn) over that concatenated vector representation @xcite . in both cases, the cited text span is determined by measuring the cosine similarities between the citing and candidate cited statements. other works, such as abura @xcite have also worked with the cl-scisumm corpus. recently, bert-based models have been deployed for the citation linkage task and are being used in many experiments. @xcite fine-tuned bert to determine the referred cited sentences from the cited document. @xcite applied a cnn over scibert-based features @xcite to determine which text span in the cited article is actually being referred. they concatenated the features from the bert-based model for feature generation. umapathy et al. (2020) utilized key-phrase similarity using the rapid automated keyword extraction algorithm @xcite and a bert-based architecture for cited text span identification. however, only a few citation linkage works are found for biomedical research papers. citation linkage for biomedical research articles is more challenging due to various representations of the same component. one notable work for this domain is from 2017, where @xcite used traditional machine learning approach over their own small expert-annotated corpus. and so far, this is the only human annotated corpus for the citation linkage task in the biomedical domain. ## biocite: description of the framework the development of the framework involves two major steps: creating a balanced automatically generated training corpus of reasonable size and building a framework for determining the referred statements from the cited document for a particular citing statement. ## experimental setup and result analysis in this section, the experimental setup and the results of the models investigated for the citation linkage task are discussed. as the human annotated test data is highly imbalanced, apart from f-1 score, matthews correlation coefficient (mcc) and balanced accuracy (bacc) are also used to assess the performance of the models. ## conclusion biomedical literature is complex in nature due to having complex biological and chemical component names. our framework, biocite, performs well when dealing with the human annotated test set containing research articles accumulated from the biomedical domain and outperforms the previous prominent works. however, there are still a few avenues to investigate. the text span used here is a single sentence. in future, it can be expanded to the paragraph level which would capture the contextual information as well. graph-based neural networks which perform well when working with paragraphs @xcite ) could be used. moreover, bert-based models can be explored as well.