Aspect-based Document Similarity for Research Papers

Malte Ostendorff, Terry Ruas, Till Blume, Bela Gipp, Georg Rehm


Abstract
Traditional document similarity measures provide a coarse-grained distinction between similar and dissimilar documents. Typically, they do not consider in what aspects two documents are similar. This limits the granularity of applications like recommender systems that rely on document similarity. In this paper, we extend similarity with aspect information by performing a pairwise document classification task. We evaluate our aspect-based document similarity approach for research papers. Paper citations indicate the aspect-based similarity, i.e., the title of a section in which a citation occurs acts as a label for the pair of citing and cited paper. We apply a series of Transformer models such as RoBERTa, ELECTRA, XLNet, and BERT variations and compare them to an LSTM baseline. We perform our experiments on two newly constructed datasets of 172,073 research paper pairs from the ACL Anthology and CORD-19 corpus. According to our results, SciBERT is the best performing system with F1-scores of up to 0.83. A qualitative analysis validates our quantitative results and indicates that aspect-based document similarity indeed leads to more fine-grained recommendations.
Anthology ID:
2020.coling-main.545
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6194–6206
Language:
URL:
https://aclanthology.org/2020.coling-main.545
DOI:
10.18653/v1/2020.coling-main.545
Bibkey:
Cite (ACL):
Malte Ostendorff, Terry Ruas, Till Blume, Bela Gipp, and Georg Rehm. 2020. Aspect-based Document Similarity for Research Papers. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6194–6206, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Aspect-based Document Similarity for Research Papers (Ostendorff et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.545.pdf
Code
 malteos/aspect-document-similarity
Data
CORD-19