An approach to estimating cited sentences in academic papers using Doc2vec
Shunsuke Tanabe, Manabu Ohta, Atsuhiro Takasu, Jun Adachi · 2018
Most academic authors refer to the literature when introducing their proposed methods and the data used in their experiments. These references can be very helpful when trying to understand a paper; however, some authors do not always state clearly the specific part of the referenced work they are referring the reader to and it can be quite laborintensive to have to read the whole document to identify the relevant information. In this paper, we propose a method for estimating the appropriate parts of a referenced work as the "cited parts," with the aim of reducing this burden. We first extract sentences in an academic paper that cites references to the literature as "citing sentences." We then vectorize the citing sentences and all the sentences in the cited papers using doc2vec and estimate the most appropriate cited part as the sentence that has the most similar feature vector to that of the citing sentence. To evaluate the proposed method, we conducted experiments using English-language papers and a questionnaire survey that asked subjects to evaluate the appropriateness of the cited parts estimated by the method. The experiments showed that this approach's success in estimating the appropriate parts of a cited paper as the cited parts depended on the citation intention of the citing sentences.