Improved Local Citation Recommendation Based on Context Enhanced with Global Information (2020)

Abstract: Local citation recommendation aims at finding articles relevant for given citation context. While most previous approaches represent context using solely text surrounding the citation, we propose enhancing context representation with global information. Specifically, we include citing article’s title and abstract into context representation. We evaluate our model on datasets with different citation context sizes and demonstrate improvements with globally-enhanced context representations when citation contexts are smaller.

## introduction the number of published scientific articles has been growing rapidly: it surpassed 50 million in 2009 @xcite and the global publication rate is still growing @xcite . as a consequence, scholars are finding it increasingly difficult to keep up with relevant research. the problem can be alleviated by citation recommendation (cr) systems, which help researchers writing articles find published work that they might consider citing. approaches to cr come in two types: global @xcite @xcite and local @xcite @xcite . global cr models typically take the article's abstract or the entire text as input (the citing article) and output a list of relevant articles (the candidates for cited articles). in contrast, local cr models take an excerpt of the citing article (the citation context) as input and recommend articles that may be cited specifically in that context. 1 although the cited article is already described by the citation context which the local cr models use to find relevant articles, we hypothesize that these models could still benefit from global information on the citing article, which provides a broader context for specific citation. for instance, consider the following citation context: foot_0 other approaches include for example clustering-based algorithms, such as the one presented in citation, or techniques which rely on building a statistical language model to rank kps, like the one presented in citation. looking at the context alone, it is difficult to tell which articles were cited at citations. however, with global information (citing article's text) available, the model could narrow the search. in this work, we address the task of local cr and experiment with enhancing the citation context with citing article's global information, more specifically with its title and abstract. we propose a model that produces the final recommendation score as a combination of semantic (based on the match between citing article's citation context and cited article's content) and bibliographic relevance (based on articles' popularity in the scientific community). our evaluation on two datasets with different citation context sizes shows that enhancing citation context with global information helps when the citation context is smaller. we also show that inclusion of bibliographic relevance leads to better results compared to model with semantic relevance only. the contribution of our work is twofold: (1) we present a model that includes global information into local cr and obtains competitive results compared to previous work @xcite and (2) we show that inclusion of global information besides citation context helps when citation contexts are smaller. ## related work compared to global cr, local cr has attracted less attention, presumably as there is fewer datasets with extracted citation contexts. a comprehensive overview of local cr is @xcite . the task of local cr was introduced by @xcite , who used tf-idf representations of contexts and cited articles in a vector similarity based setup. @xcite proposed a model that detects contexts in an article's text and recommends citations using tf-idf based matching with other contexts extracted from a corpus of articles. @xcite framed the task as statistical machine translation from context to cited article. @xcite built a system that provides recommendations while manuscript is being written. the system is based on a number of hand-crafted features extracted from both citation context and the rest of article's content. recently, deep learning models were proposed for the task. @xcite presented a neural probabilistic model that embeds words from the context and all the articles from the corpus into a shared embedding space. a neural module uses the so-obtained embeddings for determining the probability of citing an article in a given context. the model of @xcite encodes citation context and decodes it into the title of cited article, using also the information about the authors of citing and cited articles. @xcite use stacked denoising autoencoders for representing cited articles and bidirectional lstms for citation context's embedding. our approach extends on these models by enhancing the representation of citation context with global information. while there has been work that proposed adding global information to context representation @xcite , to the best of our knowledge, no such deep learning model has been proposed. ## model the proposed model takes five inputs: (1) citation context text, (2) citing article's title and abstract (henceforth: citing article's text), (3) cited article title and abstract (henceforth: candidate article's text), (4) a list of cited article authors, and (5) the number of cited article citations per last y years, together with the total number of citations. the output of the model is a recommendation score indicating whether candidate article should be cited in input context. two modules make up the model: the semantic module and the bibliographic module. we depict the two modules in figures 1 and 2 . the final recommendation score is a weighted sum of the scores produced by the two modules. the intuition behind the weighted sum of the scores is that, depending on the context, the authors might prefer to cite articles that are influential in their research community (i.e., articles with high bibliographic score) or articles that pertain to the specifics of their work (e.g., a particular article their work builds on or a specific method they use). intuitively, in the former case, the model should put more weight on the bibliographic score, while in the latter case a higher weight on semantic score would be expected. dai et al. (2019) , we use bidirectional long short-term memory (bi-lstm) cells @xcite to represent citation context, but also to represent both cited article and global information from the citing article. text fed to the module is segmented and tokenized using spacy. 3 target citation and other citations are masked with targetcit and othercit placeholders, respectively. all three textual inputs are passed through the same two layers: lstm and attention layer. ## conclusion we presented a model for local citation recommendation with citation contexts enhanced with global information, i.e., the text from citing article's title and abstract. model shows improvements over a competitive model @xcite on the refseer dataset and its variants on datasets with smaller citation context sizes. for future work, we plan to further investigate the differences in performance across different datasets.