# Explaining Relationships Between Scientific Documents

arXiv: https://arxiv.org/abs/2002.00317 · HTML: https://mlsanity.com/paper/2002.00317/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2020.

## Built on by (most cited first, 30 of 35)

- [A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery](https://mlsanity.com/paper/2406.10833/) (2024)
- [Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings](https://mlsanity.com/paper/2202.06671/) (2022)
- [LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models](https://mlsanity.com/paper/2306.12420/) (2023)
- [Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity](https://mlsanity.com/paper/2111.08366/) (2021)
- [ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews](https://mlsanity.com/paper/2306.12587/) (2023)
- [CHIME: LLM-Assisted Hierarchical Organization of Scientific Studies for Literature Review Support](https://mlsanity.com/paper/2407.16148/) (2024)
- [CiteBench: A benchmark for Scientific Citation Text Generation](https://mlsanity.com/paper/2212.09577/) (2022)
- [PaperWeaver: Enriching Topical Paper Alerts by Contextualizing Recommended Papers with User-collected Papers](https://mlsanity.com/paper/2403.02939/) (2024)
- [CORWA: A Citation-Oriented Related Work Annotation Dataset](https://mlsanity.com/paper/2205.03512/) (2022)
- [Related Work and Citation Text Generation: A Survey](https://mlsanity.com/paper/2404.11588/) (2024)
- [Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding](https://mlsanity.com/paper/2305.14232/) (2023)
- [A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents](https://mlsanity.com/paper/2305.14772/) (2023)
- [CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision](https://mlsanity.com/paper/2205.06207/) (2022)
- [Multi-Document Scientific Summarization from a Knowledge Graph-Centric View](https://mlsanity.com/paper/2209.04319/) (2022)
- [Hierarchical Catalogue Generation for Literature Review: A Benchmark](https://mlsanity.com/paper/2304.03512/) (2023)
- [Automatic Related Work Generation: A Meta Study](https://mlsanity.com/paper/2201.01880/) (2022)
- [Systematic Task Exploration with LLMs: A Study in Citation Text Generation](https://mlsanity.com/paper/2407.04046/) (2024)
- [Advancements in Scientific Controllable Text Generation Methods](https://mlsanity.com/paper/2307.05538/) (2023)
- [Tree-of-Debate: Multi-Persona Debate Trees Elicit Critical Thinking for Scientific Comparative Analysis](https://mlsanity.com/paper/2502.14767/) (2025)
- [Expert Preference-based Evaluation of Automated Related Work Generation](https://mlsanity.com/paper/2508.07955/) (2025)
- [Gaps or Hallucinations? Gazing into Machine-Generated Legal Analysis for Fine-grained Text Evaluations](https://mlsanity.com/paper/2409.09947/) (2024)
- [Large-scale Evaluation of Transformer-based Article Encoders on the Task of Citation Recommendation](https://mlsanity.com/paper/2209.05452/) (2022)
- [Explaining Relationships Among Research Papers](https://mlsanity.com/paper/2402.13426/) (2024)
- [Contextualizing Generated Citation Texts](https://mlsanity.com/paper/2402.18054/) (2024)
- [Multi-Facet Blending for Faceted Query-by-Example Retrieval](https://mlsanity.com/paper/2412.01443/) (2024)
- [The Noisy Path from Source to Citation: Measuring How Scholars Engage with Past Research](https://mlsanity.com/paper/2502.20581/) (2025)
- [SCIRGC: Multi-Granularity Citation Recommendation and Citation Sentence Preference Alignment](https://mlsanity.com/paper/2505.20103/) (2025)
- [Modelling and Classifying the Components of a Literature Review](https://mlsanity.com/paper/2508.04337/) (2025)
- [How Data Scientists Review the Scholarly Literature](https://mlsanity.com/paper/2301.03774/) (2023)
- [Cited Text Spans for Citation Text Generation](https://mlsanity.com/paper/2309.06365/) (2023)

## Abstract (arXiv)

We address the task of explaining relationships between two scientific documents using natural language text. This task requires modeling the complex content of long technical documents, deducing a relationship between these documents, and expressing the details of that relationship in text. In addition to the theoretical interest of this task, successful solutions can help improve researcher efficiency in search and review. In this paper we establish a dataset of 622K examples from 154K documents. We pretrain a large language model to serve as the foundation for autoregressive approaches to the task. We explore the impact of taking different views on the two documents, including the use of dense representations extracted with scientific IE systems. We provide extensive automatic and human evaluations which show the promise of such models, but make clear challenges for future work.