# CORWA: A Citation-Oriented Related Work Annotation Dataset

arXiv: https://arxiv.org/abs/2205.03512 · HTML: https://mlsanity.com/paper/2205.03512/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 69 references that are in the map.
- First posted on arXiv 7 May 2022.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Structural Scaffolds for Citation Intent Classification in Scientific Publications](https://mlsanity.com/paper/1904.01608/) (2019)
- [PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts](https://mlsanity.com/paper/1710.06071/) (2017)
- [ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks](https://mlsanity.com/paper/1909.01716/) (2019)
- [Graph-based Neural Multi-Document Summarization](https://mlsanity.com/paper/1706.06681/) (2017)
- [Explaining Relationships Between Scientific Documents](https://mlsanity.com/paper/2002.00317/) (2020)
- [MultiCite: Modeling realistic citations requires moving beyond the single-sentence single-label setting](https://mlsanity.com/paper/2107.00414/) (2021)
- [A Paragraph-level Multi-task Learning Model for Scientific Fact-Verification](https://mlsanity.com/paper/2012.14500/) (2020)
- [Scientific Discourse Tagging for Evidence Extraction](https://mlsanity.com/paper/1909.04758/) (2019)
- [The CL-SciSumm Shared Task 2018: Results and Key Insights](https://mlsanity.com/paper/1909.00764/) (2019)
- [Automatic Related Work Generation: A Meta Study](https://mlsanity.com/paper/2201.01880/) (2022)
- [Article citation study: Context enhanced citation sentiment detection](https://mlsanity.com/paper/2005.04534/) (2020)

## Built on by (most cited first, 17 of 17)

- [Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://mlsanity.com/paper/2502.05151/) (2025)
- [ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews](https://mlsanity.com/paper/2306.12587/) (2023)
- [CiteBench: A benchmark for Scientific Citation Text Generation](https://mlsanity.com/paper/2212.09577/) (2022)
- [Related Work and Citation Text Generation: A Survey](https://mlsanity.com/paper/2404.11588/) (2024)
- [ALiiCE: Evaluating Positional Fine-grained Citation Generation](https://mlsanity.com/paper/2406.13375/) (2024)
- [LitLLMs, LLMs for Literature Review: Are we there yet?](https://mlsanity.com/paper/2412.15249/) (2024)
- [Systematic Task Exploration with LLMs: A Study in Citation Text Generation](https://mlsanity.com/paper/2407.04046/) (2024)
- [Expert Preference-based Evaluation of Automated Related Work Generation](https://mlsanity.com/paper/2508.07955/) (2025)
- [Select, Read, and Write: A Multi-Agent Framework of Full-Text-based Related Work Generation](https://mlsanity.com/paper/2505.19647/) (2025)
- [Explaining Relationships Among Research Papers](https://mlsanity.com/paper/2402.13426/) (2024)
- [Contextualizing Generated Citation Texts](https://mlsanity.com/paper/2402.18054/) (2024)
- [The Noisy Path from Source to Citation: Measuring How Scholars Engage with Past Research](https://mlsanity.com/paper/2502.20581/) (2025)
- [Modelling and Classifying the Components of a Literature Review](https://mlsanity.com/paper/2508.04337/) (2025)
- [Paragraph-level Citation Recommendation based on Topic Sentences as Queries](https://mlsanity.com/paper/2305.12190/) (2023)
- [Cited Text Spans for Citation Text Generation](https://mlsanity.com/paper/2309.06365/) (2023)
- [Improving Citation Text Generation: Overcoming Limitations in Length Control](https://mlsanity.com/paper/2407.14997/) (2024)
- [The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale](https://mlsanity.com/paper/2605.15011/) (2026)

## Abstract (arXiv)

Academic research is an exploratory activity to discover new solutions to problems. By this nature, academic research works perform literature reviews to distinguish their novelties from prior work. In natural language processing, this literature review is usually conducted under the "Related Work" section. The task of related work generation aims to automatically generate the related work section given the rest of the research paper and a list of papers to cite. Prior work on this task has focused on the sentence as the basic unit of generation, neglecting the fact that related work sections consist of variable length text fragments derived from different information sources. As a first step toward a linguistically-motivated related work generation framework, we present a Citation Oriented Related Work Annotation (CORWA) dataset that labels different types of citation text fragments from different information sources. We train a strong baseline model that automatically tags the CORWA labels on massive unlabeled related work section texts. We further suggest a novel framework for human-in-the-loop, iterative, abstractive related work generation.