# Related Work and Citation Text Generation: A Survey

arXiv: https://arxiv.org/abs/2404.11588 · HTML: https://mlsanity.com/paper/2404.11588/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 61 references that are in the map.
- First posted on arXiv 17 Apr 2024.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies](https://mlsanity.com/paper/1804.11283/) (2018)
- [Neural Text Summarization: A Critical Evaluation](https://mlsanity.com/paper/1908.08960/) (2019)
- [Faithful to the Original: Fact Aware Neural Abstractive Summarization](https://mlsanity.com/paper/1711.04434/) (2017)
- [Structural Scaffolds for Citation Intent Classification in Scientific Publications](https://mlsanity.com/paper/1904.01608/) (2019)
- [Assessing The Factual Accuracy of Generated Text](https://mlsanity.com/paper/1905.13322/) (2019)
- [ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks](https://mlsanity.com/paper/1909.01716/) (2019)
- [Explaining Relationships Between Scientific Documents](https://mlsanity.com/paper/2002.00317/) (2020)
- [Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents](https://mlsanity.com/paper/2106.00130/) (2021)
- [MultiCite: Modeling realistic citations requires moving beyond the single-sentence single-label setting](https://mlsanity.com/paper/2107.00414/) (2021)
- [Target-aware Abstractive Related Work Generation with Contrastive Learning](https://mlsanity.com/paper/2205.13339/) (2022)
- [CORWA: A Citation-Oriented Related Work Annotation Dataset](https://mlsanity.com/paper/2205.03512/) (2022)
- [Shallow Synthesis of Knowledge in GPT-Generated Texts: A Case Study in Automatic Related Work Composition](https://mlsanity.com/paper/2402.12255/) (2024)
- [Neural Related Work Summarization with a Joint Context-driven Attention Mechanism](https://mlsanity.com/paper/1901.09492/) (2019)
- [Causal Intervention for Abstractive Related Work Generation](https://mlsanity.com/paper/2305.13685/) (2023)
- [Contextualizing Generated Citation Texts](https://mlsanity.com/paper/2402.18054/) (2024)

## 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)
- [ALiiCE: Evaluating Positional Fine-grained Citation Generation](https://mlsanity.com/paper/2406.13375/) (2024)
- [HypER: Literature-grounded Hypothesis Generation and Distillation with Provenance](https://mlsanity.com/paper/2506.12937/) (2025)
- [From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems](https://mlsanity.com/paper/2503.01424/) (2025)
- [OARelatedWork: A Large-Scale Dataset of Related Work Sections with Full-texts from Open Access Sources](https://mlsanity.com/paper/2405.01930/) (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)
- [Towards AI-assisted Academic Writing](https://mlsanity.com/paper/2503.13771/) (2025)
- [Modelling and Classifying the Components of a Literature Review](https://mlsanity.com/paper/2508.04337/) (2025)
- [AutoPR: Let's Automate Your Academic Promotion!](https://mlsanity.com/paper/2510.09558/) (2025)
- [Reward Modeling for Scientific Writing Evaluation](https://mlsanity.com/paper/2601.11374/) (2026)
- [MCiteBench: A Multimodal Benchmark for Generating Text with Citations](https://mlsanity.com/paper/2503.02589/) (2025)
- [Accepted with Minor Revisions: Value of AI-Assisted Scientific Writing](https://mlsanity.com/paper/2511.12529/) (2025)
- [The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale](https://mlsanity.com/paper/2605.15011/) (2026)
- [RWGBench: Evaluating Scholarly Positioning in Related Work Generation](https://mlsanity.com/paper/2606.24894/) (2026)
- [Assembling the CREW: A Collaborative Multi-agent Reinforcement Learning Framework for Automated Related Work Generation](https://mlsanity.com/paper/2609.15721/) (2026)
- [Does AI-Generated Scientific Text Follow Human Argumentation Patterns? A CARS-Based Comparison of Research Article Introductions](https://mlsanity.com/paper/2610.01353/) (2026)

## Abstract (arXiv)

To convince readers of the novelty of their research paper, authors must perform a literature review and compose a coherent story that connects and relates prior works to the current work. This challenging nature of literature review writing makes automatic related work generation (RWG) academically and computationally interesting, and also makes it an excellent test bed for examining the capability of SOTA natural language processing (NLP) models. Since the initial proposal of the RWG task, its popularity has waxed and waned, following the capabilities of mainstream NLP approaches. In this work, we survey the zoo of RWG historical works, summarizing the key approaches and task definitions and discussing the ongoing challenges of RWG.