# Aspect-based Document Similarity for Research Papers

arXiv: https://arxiv.org/abs/2010.06395 · HTML: https://mlsanity.com/paper/2010.06395/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 21 of 21)

- [Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings](https://mlsanity.com/paper/2202.06671/) (2022)
- [Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity](https://mlsanity.com/paper/2111.08366/) (2021)
- [HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information](https://mlsanity.com/paper/2203.09629/) (2022)
- [CSFCube -- A Test Collection of Computer Science Research Articles for Faceted Query by Example](https://mlsanity.com/paper/2103.12906/) (2021)
- [Identifying Machine-Paraphrased Plagiarism](https://mlsanity.com/paper/2103.11909/) (2021)
- [A Domain-adaptive Pre-training Approach for Language Bias Detection in News](https://mlsanity.com/paper/2205.10773/) (2022)
- [Specialized Document Embeddings for Aspect-based Similarity of Research Papers](https://mlsanity.com/paper/2203.14541/) (2022)
- [Testing the Generalization of Neural Language Models for COVID-19 Misinformation Detection](https://mlsanity.com/paper/2111.07819/) (2021)
- [Augmenting Scientific Creativity with Retrieval across Knowledge Domains](https://mlsanity.com/paper/2206.01328/) (2022)
- [AspectCSE: Sentence Embeddings for Aspect-based Semantic Textual Similarity Using Contrastive Learning and Structured Knowledge](https://mlsanity.com/paper/2307.07851/) (2023)
- [Analyzing Multi-Task Learning for Abstractive Text Summarization](https://mlsanity.com/paper/2210.14606/) (2022)
- [Encoding Multi-Domain Scientific Papers by Ensembling Multiple CLS Tokens](https://mlsanity.com/paper/2309.04333/) (2023)
- [Multi-Facet Blending for Faceted Query-by-Example Retrieval](https://mlsanity.com/paper/2412.01443/) (2024)
- [Rating and aspect-based opinion graph embeddings for explainable recommendations](https://mlsanity.com/paper/2107.03385/) (2021)
- [The Inefficiency of Language Models in Scholarly Retrieval: An Experimental Walk-through](https://mlsanity.com/paper/2203.15364/) (2022)
- [Mapping Researcher Activity based on Publication Data by means of Transformers](https://mlsanity.com/paper/2306.09049/) (2023)
- [SemCSE-Multi: Multifaceted and Decodable Embeddings for Aspect-Specific and Interpretable Scientific Domain Mapping](https://mlsanity.com/paper/2510.11599/) (2025)
- [Graphing else matters: exploiting aspect opinions and ratings in explainable graph-based recommendations](https://mlsanity.com/paper/2107.03226/) (2021)
- [The Critical Role of Aspects in Measuring Document Similarity](https://mlsanity.com/paper/2601.03435/) (2026)
- [GRAFT: Graph-Distilled Generative Retrieval for Facet-Aware Scientific Literature Exploration](https://mlsanity.com/paper/2608.22381/) (2026)
- [Same Problem, Different Field: Cross-Domain Solution Import via Domain-Stripped Computational Fingerprints](https://mlsanity.com/paper/2609.07595/) (2026)

## Abstract (arXiv)

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 for research papers. Paper citations indicate the aspect-based similarity, i.e., the section title 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. Our results show SciBERT as the best performing system. A qualitative examination validates our quantitative results. Our findings motivate future research of aspect-based document similarity and the development of a recommender system based on the evaluated techniques. We make our datasets, code, and trained models publicly available.