# SciReviewGen: A Large-scale Dataset for Automatic Literature Review Generation

arXiv: https://arxiv.org/abs/2305.15186 · HTML: https://mlsanity.com/paper/2305.15186/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 69 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (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)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022)
- [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://mlsanity.com/paper/1804.05685/) (2018)
- [Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model](https://mlsanity.com/paper/1906.01749/) (2019)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [A Comprehensive Survey of Deep Learning for Image Captioning](https://mlsanity.com/paper/1810.04020/) (2018)
- [Deep Learning on Image Denoising: An overview](https://mlsanity.com/paper/1912.13171/) (2019)
- [Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles](https://mlsanity.com/paper/2010.14235/) (2020)
- [Aspect Level Sentiment Classification with Deep Memory Network](https://mlsanity.com/paper/1605.08900/) (2016)
- [MS2: Multi-Document Summarization of Medical Studies](https://mlsanity.com/paper/2104.06486/) (2021)
- [ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks](https://mlsanity.com/paper/1909.01716/) (2019)
- [Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references](https://mlsanity.com/paper/1402.4578/) (2014)
- [Exploring Neural Models for Query-Focused Summarization](https://mlsanity.com/paper/2112.07637/) (2021)
- [Effective Use of Word Order for Text Categorization with Convolutional Neural Networks](https://mlsanity.com/paper/1412.1058/) (2014)
- [Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings](https://mlsanity.com/paper/1602.02373/) (2016)
- [A Survey on Deep Learning Architectures for Image-based Depth Reconstruction](https://mlsanity.com/paper/1906.06113/) (2019)

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

- [ResearchQA: Evaluating Scholarly Question Answering at Scale Across 75 Fields with Survey-Mined Questions and Rubrics](https://mlsanity.com/paper/2509.00496/) (2025)
- [SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature](https://mlsanity.com/paper/2406.07835/) (2024)
- [SciArena: An Open Evaluation Platform for Non-Verifiable Scientific Literature-Grounded Tasks](https://mlsanity.com/paper/2507.01001/) (2025)
- [LiRA: A Multi-Agent Framework for Reliable and Readable Literature Review Generation](https://mlsanity.com/paper/2510.05138/) (2025)
- [Evolving Roles of LLMs in Scientific Innovation: Assistant, Collaborator, Scientist, and Evaluator](https://mlsanity.com/paper/2507.11810/) (2025)
- [Systematic Task Exploration with LLMs: A Study in Citation Text Generation](https://mlsanity.com/paper/2407.04046/) (2024)
- [From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems](https://mlsanity.com/paper/2503.01424/) (2025)
- [Automated Review Generation Method Based on Large Language Models](https://mlsanity.com/paper/2407.20906/) (2024)
- [SurveyGen: Quality-Aware Scientific Survey Generation with Large Language Models](https://mlsanity.com/paper/2508.17647/) (2025)
- [SurveyGen-I: Consistent Scientific Survey Generation with Evolving Plans and Memory-Guided Writing](https://mlsanity.com/paper/2508.14317/) (2025)
- [In-depth Research Impact Summarization through Fine-Grained Temporal Citation Analysis](https://mlsanity.com/paper/2505.14838/) (2025)
- [Agentic AI-Empowered Dynamic Survey Framework](https://mlsanity.com/paper/2602.04071/) (2026)
- [SurveyLens: A Discipline-Aware Benchmark for Automatic Survey Generation](https://mlsanity.com/paper/2602.11238/) (2026)
- [An Artificial Intelligence Driven Semantic Similarity-Based Pipeline for Rapid Literature](https://mlsanity.com/paper/2509.15292/) (2025)
- [How Much Structure Do LLMs Need? Evaluating LLMs for Bibliometric Cluster Description](https://mlsanity.com/paper/2605.24351/) (2026)
- [LitReview Arena: Evaluating Literature Review Agents with Battle-Style Peer Review Platform](https://mlsanity.com/paper/2608.21374/) (2026)
- [SurveyAgent-HKA: A multi-agent framework for scientific survey generation with LLMs and human knowledge augmentation](https://mlsanity.com/paper/2609.05938/) (2026)

## Abstract (arXiv)

Automatic literature review generation is one of the most challenging tasks in natural language processing. Although large language models have tackled literature review generation, the absence of large-scale datasets has been a stumbling block to the progress. We release SciReviewGen, consisting of over 10,000 literature reviews and 690,000 papers cited in the reviews. Based on the dataset, we evaluate recent transformer-based summarization models on the literature review generation task, including Fusion-in-Decoder extended for literature review generation. Human evaluation results show that some machine-generated summaries are comparable to human-written reviews, while revealing the challenges of automatic literature review generation such as hallucinations and a lack of detailed information. Our dataset and code are available at https://github.com/tetsu9923/SciReviewGen.