Citation Sentence Generation Leveraging the Content of Cited Papers (2022)

Abstract: We address automatic citation sentence generation, which reduces the burden on writing scientific papers. For highly accurate citation senetence generation, appropriate language must be learned using information such as the relationship between the cited source and the cited paper as well as the context in which the paper cited. Although the abstracts of papers have been used for the generation in the past, they often contain extra information in the citation sentence, which might negatively impact the generation of citation sentences. Therefore, this study attempts to learn a highly accurate citation sentence generation model using sentences from cited articles that resemble the previous sentence to the cited location, thereby utilizing information that is more useful for citation sentence generation.

## introduction in recent years, the use of such preprint servers as arxiv @xcite has increased the amount of scientific literature. with this, we need a lot of citations to write a new paper and writing the related work section has become timeconsuming. the development of automatic citation sentence generation system can support the writing of papers and relieve scientific researcher's burden on tracking and editing citations @xcite . there have been several studies on citation sentence generation. hoang and kan (2010) constructed a keyword-based tree from the cited papers and utilized to generate citation sentences. @xcite used a multi-source pointergenerator network with cross attention mechanism to generate a single citation sentence for a single citation. @xcite used the fution-in-decoder (fid) model @xcite to generate citation sentences for citing multiple papers, which is commonplace in real papers. they also consider differences in citation intent @xcite . there are many different relationships between citing paper and the cited papers. ## proposed method citation generation is the task of generating the citation sentence to describe a cited paper under the context in a citing paper. figure 1 illustrates an overview of our proposed method. in the training phase, the mehod consists of three steps: a) preparing the full text of the cited papers contained in the citation sentences in the training data; b) extracting semantically similar sentences to each citation sentence from the cited papers using cosine similarity; c) learning to generate citation sentence from the semantically similar sentences. in the prediction phase, the mehod consists of three steps: a) preparing the full text of the cited papers contained in the quoted sentences in the test data; d) extracting sentences from the cited papers that are semantically similar to the previous sentence in the target citation using cosine similarity; c) learning to generate citations from semantically similar sentences. the major difference between training and prediction is in steps b) and d). in step b) of training phase, the system extracts sentences from the cited papers, that are similar to the citation sentence and useful for generating the citation sentence. on the other hand, in step d) of prediction phase, the system extracts the two sentences immediately before the citation sentence, because we cannot use the citation sentence, which is the sentence itself to generate and does not exist in the phase. to utilize the all sentences of a cited paper, excluding its abstract, the text is divided into sentences using nltk @xcite , and we calculated the embedded representation of each sentence using sentencebert @xcite . in the step c), we performe fine-tuning a pretrained model for generating citation sentences. we used t5 @xcite as a pre-trained model. ## experiments we observed changes in the accuracy of the generated citation sentences by combining the citation intent, the citing paper's abstract, the citation context, the cited paper's abstract, and the cited paper's content. then we investigated which information contributes to the generation of citation sentences. ## conclusion we performed the task of generating an appropriate citation sentence from a citing paper, cited papers, and the citation context. while citation sentence generation in previous studies has been based on sentences in abstracts, we proposed citation sentence generation based on sentences in the citing paper and the cited papers. experimental results show that our proposed method is more accurate in generating citation sentences than the conventional method of using sentences in abstracts. in the future, we will evaluate using people or other methods than rouge and larger citation datasets.