C ite QA @ CLS ci S umm 2020 (2020)

Abstract: In academic publications, citations are used to build context for a concept by highlighting relevant aspects from reference papers. Automatically identifying referenced snippets can help researchers swiftly isolate principal contributions of scientific works. In this paper, we exploit the underlying structure of scientific articles to predict reference paper spans and facets corresponding to a citation. We propose two methods to detect citation spans - keyphrase overlap, BERT along with structural priors. We fine-tune FastText embeddings and leverage textual, positional features to predict citation facets.

## introduction with the ballooning growth in the number of research papers published every year @xcite , being able to automatically identify the main contributions of a paper would be a useful tool to aid the ingestion of academic works. given that citations are expected to focus on the important or note-worthy aspects of a paper @xcite , tagging a citation with its corresponding reference paper snippet could help identify the main contributions of the reference paper. while methodologies to find influential papers exist, there is currently no established method to identify the aspects of a reference paper that make it influential. uncovering important aspects has a range of downstream applications such as discovering interesting insights about salient, frequently cited concepts or empowering researchers to quickly read and understand a large volume of papers. ability to quickly sift through a large volume of papers is especially useful during times of crisis to get equipped with the most relevant research in a new area . external to research papers, this tool could also be leveraged by journalists and paralegals as news articles and legal documents follow a similar citation structure. in this work, we tackle two tasks -citation span detection and facet identification. we utilize the structural characteristics of scientific articles such as section and sentence importance, location of sentences in conjunction with textual features. we present a keyphrase extraction and a bert model for citation identification and augment sentence embeddings with hand-crafted features to classify the citation onto a set of pre-defined facets foot_0 . ## task and dataset description we break down our objective into two sub-tasks: citation span detection and facet identification. a. span detection -given a reference paper (rp), a citing paper (cp), and the citation sentence (citance), identify the spans in the reference paper which most accurately capture the citation. b. facet identification -given reference and citing papers, perform a multi-label citation classification onto 5 pre-defined facets {method, result, aim, hypothesis, im-plication} indicating the type of citation. for both task a and task b, we make use of the dataset released as part of the shared task at cl-scisumm @xcite for emnlp 2020. the cl-scisumm dataset contains a set of 40 reference papers (in the domain of computational linguistics), each paired with up to 35 citing papers, totalling 753 unique citations. the dataset introduces two tasks: 1) span detection(a) and facet identification(b) , and 2) summarization. in this work, we focus on task 1. each citation is tagged with the gold reference spans and one or more facets. there is, however, no inter-annotator agreement or human performance reported on this dataset. an example from our dataset is shown below. citing sentence -given that close to 95% of the word occurrences in human labeled data are tagged with their most frequent part of speech @xcite reference sentence -simply assigning to each word its most frequent associated tag in a corpus achieves 94.6% accuracy on the wsj portion of the penn treebank facet -result we also make use of scisummnet @xcite and a cleaned version @xcite of the acl-arc corpus @xcite for pretraining our models. scisummnet contains over 1000 reference papers auto-annotated with citation spans and acl-arc corpus contains over 10k articles from acl anthology. we utilize scisummnet to fine-tune our bert @xcite model to adapt to 'scholarly document' style of text and use acl-arc corpus to generate domain-specific word embeddings using fasttext @xcite . ## related work prior work on task a can be broadly classified into two categories -text similarity and deep learning based methods. text similarity methods typically compute similarity scores between each reference sentence and the citing sentence and rank them to predict the reference spans. similarity can be computed in different ways -baruah (2018) use a word-embedding cosine similarity while syed (2019) and abura'ed (2018) use multiple similarity metrics like jaccard, bm25, tf-idf as features to train a classifier to predict the best reference sentence. polyu @xcite groups sentences into chunks and performs predictions by using a ranksvm over these chunks. deep learning models such as citelistnet (kim, 2019) and nactem-uom @xcite learn textual feature representations for classification. citelistnet employs a text-similarity phase followed by a cnn reranker while nactem-uom trains bert to score a reference, citing sentence pair and predicts top-3 reference sentences. for the facet identification task, wang (2018) use bag of words in conjunction with some sentence features in multiple facet-specific classifiers, baruah (2018) use average word2vec embeddings for prediction, and @xcite use bag of words with random forests. a common theme across previous works was that they predominantly only utilized the paper text but citations often depend on external factors like section of sentence, position in section etc. we augment our models with these biases to perform a structure aware citation span and facet detection. ## proposed approach in this section, we briefly describe our data preprocessing and the models used for span and facet identification. ## experimental setup we fine-tune bert base on masked language modeling and sequence classification tasks using the transformers library foot_1 for 2-6 epochs (with early stopping) on an nvidia t4 tensor core gpu and optimize our models using adam @xcite . we use 32 out of 40 papers present in the 2018 training data to fine-tune our model on sentence pair classification, reserving the remaining 8 papers for validation. we chose not to use the automatically annotated 2019 training data as we noticed a drop in performance on the validation set. the section and sentence importance bias coefficients were hyperparameters and were varied across runs. our code repository contains more details on the exact hyperparameter values. ## results ## conclusion in this work, we show that application of biases exploiting the underlying structure of scientific texts is useful on the tasks of citation span and facet identification. in the future, we hope to incorporate these biases into the training process instead of interpolating them during evaluation.